In the cellular Internet of Things (CIoT), it is crucial to ensure the information freshness for status update applications. Considering the centralized access methods could cause large access delay and hamper timely status updates, this paper exploits the random access method and studies decentralized status update schemes to minimize the average age of information (AoI) for CIoT. However, due to the non-cooperation among machine type communication devices (MTCDs) in random access, packet collisions are inevitable, which makes it tricky to improve the AoI performance. In this regard, we design novel age-based status update schemes to control the transmission behavior of MTCDs, where the AoI at the MTCDs and the base station (BS) is used. We first model the AoI minimization problem as a Markov decision process. Then, through variable substitution and linear programming, we get a slightly more computationally complex status update scheme, where the dual threshold structure of the scheme is proved theoretically. To facilitate system design and reduce computational complexity, we further design a low-complexity scheme, where the age thresholds at both the MTCDs and BS are optimized. Simulation results verify that the proposed schemes significantly outperform the common access scheme.

Complex status updates have attracted widespread attention in real-time monitoring services (e.g., real-time fire gas monitoring and wildfire spread prediction). In complex status updates, the status information needs to be obtained by processing the perceived original data. However, as lightweight terminals, temporarily deployed Internet of Things (IoT) devices have no computing modules. Unmanned aerial vehicle (UAV) can act as a edge server to help IoT devices complete computing tasks by mobile-edge computing (MEC). To this end, this article considers a complex status update in UAV-assisted IoT, where an UAV moves in hovering-flight-hovering mode to ensure that it can serve IoT devices in different areas. When the UAV hovers, it obtains the status information based on the original data transmitted by the IoT device and sends it to the control center. During the complex status update, the short packet communication and time-varying channel are considered. To realize the tradeoff optimization of the average Age of Information (AoI) and average power consumption of both IoT device and UAV within a long time, we formulate a location and dynamic status update strategy optimization problem for UAV hovering-flight-hovering mode. In order to solve the problem with Markov properties, we derive the state probability equations and further establish the linear programming problem with fixed UAV location. Then, we propose a probability-based algorithm to obtain UAV location and dynamic status update strategy. To adapt to more urgent scenarios, we propose an AoI threshold-based strategy to reduce the complexity of the problem. State probability equations are derived under the strategy and a linear programming problem with a fixed AoI threshold is established. Next, we propose a low-complexity algorithm to obtain the optimal AoI threshold. Simulation results show that the proposed algorithms can optimize the three performance metrics in a balanced way and we need to select the appropriate transmit power of the IoT device and the computing capacity of the UAV to achieve better performance.

This article is motivated by the requirement of high information freshness in the industrial Internet of Things (IIoT). An industrial robot sends short status packets to a control center (CC), and the timeliness of status updates is measured by the Age of Information (AoI). Due to the dynamic change of the wireless channel, the robot needs to send a pilot for channel estimation during each coherence time. Considering the robot is energy-limited, we investigate the average AoI minimization scheme for short-packet communications under the average power consumption constraint. By rationally analyzing state transitions, we first formulate the problem as a constrained Markov decision process and obtain the optimal solution through linear programming (LP). Then, for the problem of high computational complexity caused by too many variables in LP, we propose a heuristic threshold-based status update scheme by exploiting the threshold structure of the optimal solution. Simulation results show that the LP scheme can effectively minimize the average AoI and the threshold-based scheme can achieve near-optimal performance. Interestingly, we find that when the channel suffers from severe fading, at the end of a coherence time, the robot does not send status packets even if the information at the CC is particularly stale.

Motivated by the high information freshness requirement in the Internet of Things (IoT), this paper investigates adaptive packet length adjustment schemes to minimize the average age of information (AoI) for status update systems, where a machine type communication device adaptively adjusts the packet length in real time by exploiting the channel state information and AoI. Since the status packets in the IoT are often short, a significant packet error rate is introduced. Due to the instability of channel fading and the high packet error rate, optimizing the AoI performance is tricky. Under a power consumption constraint, the AoI minimization problem is modeled as a constrained Markov decision process (CMDP), and the structure of the optimal scheme is revealed. Then, under the CMDP framework, this paper transforms the AoI minimization problem into a linear programming problem and proposes a probabilistic packet length adjustment scheme, which can lead to the optimal solution. When the power consumption constraint is loose, a low-complexity suboptimal scheme is further proposed, where the expected average AoI of one period length is minimized. Simulation results verify the superiority of the proposed optimal scheme and show that the proposed low-complexity scheme can reach near-optimal performance.

Timely status updates are significant for some critical Internet of Things applications. However, due to the transmission delay of status packets, the status information arriving at the destination is not fresh enough. Especially, when the transmission delay is large, the destination cannot know the current status of the target well. In this article, the prediction technology is introduced into status update systems to solve this problem. First, we study a prediction-based status update scheme for machine-type communications (MTCs) and use the recently proposed metric, Age of Information (AoI), to characterize the status update performance. With the scarce spectrum resources, an MTC device (MTCD), as an unlicensed user, adopts the overlay mode to reuse the spectrum resource of the primary user and sends status packets to the central controller (CC). Then, we consider prediction errors and packet decoding errors to characterize the reliability of MTC, analyze the status update process in the prediction-based scheme, and derive the closed-form expression of the average AoI. Finally, in the proposed prediction-based scheme, we analyze the tradeoff between the status update performance and energy consumption and design an optimization algorithm to improve the status update performance by adjusting the transmit power and prediction horizon of the MTCD. Simulation results verify the correctness of the theoretical analysis and show that the proposed scheme can help the MTCD effectively improve the status update performance.

In this article, we investigate the performance of massive machine type communications (mMTC) in status update systems, where massive machine type communication devices (MTCDs) send status packets to the BS for system monitoring. However, massive MTCDs sending status packets to the BS will cause severe packet collisions, which will have a negative impact on status update performance. In this case, it is necessary to carry out reasonable access control and resource allocation scheme to improve the status update performance for mMTC. In this article, taking the features of mMTC into consideration, we first analyze access control, packet collisions and packet errors in mMTC respectively, and derive the closed-form expression of the average age of information for all MTCDs as the performance metric, and then propose a joint access control, frame division and subchannel allocation scheme to improve the overall status update performance. Simulation and numerical results verify the correctness of theoretical results and show that our proposed scheme can achieve almost the same performance as the exhaustive search method and outperforms benchmark schemes.