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.

Unmanned aerial vehicles (UAVs) can utilize multiaccess edge computing (MEC) to help Internet of Things (IoT) devices complete the complex status update by efficient offloading and proper trajectory design. However, considering that IoT devices usually communicate with UAVs in the finite blocklength regime, the uplink transmission cannot be error free. Due to the nonzero packet error probability (PEP), it is difficult to evaluate the instantaneous system performance as in the case with small blocklength. Moreover, the PEP is simultaneously coupled with the offloading parameters and trajectories of UAVs, which makes the performance optimization even more challenging. To this end, we first derive the analytical expressions of the average peak Age of Information (AoI), the average energy consumption of IoT devices and the average energy consumption of UAVs. Then, we formulate a joint optimization problem aiming to minimize the weighted sum of the three performance metrics by jointly optimizing the offloading parameters and the UAV trajectories. By dividing the original problem into multiple subproblems, an alternating optimization-based algorithm is proposed to solve it suboptimally. Simulation results validate the effectiveness of our proposed algorithm and reveal that by properly setting the transmit power and computing capacity of IoT devices, the desired tradeoff among the three performance metrics can be achieved and thus the system performance can be improved effectively.