Abstract
The status information of complex status update applications (e.g., real-time environmental monitoring in disaster areas) can only be obtained after data processing. Therefore, the impact of data processing on age of information (AoI) cannot be ignored in complex status update applications. In this paper, complex status updates in emergency scenario are investigated, where some multiantenna unmanned aerial vehicles (UAVs) help resource-limited user devices (UDs) complete complex status updates which involve computing processes. To reduce average AoI, UDs’ energy consumptions, and UAVs’ energy consumptions in a balanced manner, the weighted sum of the three metrics is aimed to be minimized. Scheduling strategies (i.e., data transmission and computing start moments, service period, and flight radius) and power (i.e., data and pilot transmit power) allocation strategies are jointly optimized to reduce the weighted sum. The original problem is NP-hard. Thus, it is decoupled into some subproblems. Then, the near-optimal solution or stationary point of each subproblem is obtained by analyzing the characteristics of the subproblems. Finally, an iterative algorithm is proposed to alternately optimize the scheduling and power allocation strategies to obtain the suboptimal solution of the original problem. Simulation results show that the proposed algorithm can effectively reduce the average AoI and the energy consumptions of UDs and UAVs in a balanced way.
Highlights
The steady development of Internet of Things (IoT) has promoted the demand for real-time status update applications
In order to improve the efficiency of status update with low energy consumption, we aim to minimize the weighted sum of the average age of information (AoI) and the energy consumptions of user devices (UDs) and unmanned aerial vehicles (UAVs) by jointly optimizing the scheduling and power allocation strategies, where the scheduling strategies include the transmission and computing start moments, service period, and flight radius
In order to narrow the numerical gap between the average AoI and the energy consumptions of UDs and UAVs, we design weighted factors ωA, ωE, and ωU based on the initial values of the average AoI and the energy consumptions of UDs and UAVs
Summary
The steady development of Internet of Things (IoT) has promoted the demand for real-time status update applications. In these applications, the user device (UD) (e.g., IoT device) sends sensing data to the control terminal for subsequent control processes [1]. Some works studied optimizations for AoI [2,3,4,5,6]. Some works analyzed and optimized AoI in multiaccess edge computing (MEC) systems [12,13,14,15,16,17]. The works above studied the AoI in MEC systems where the computing servers are fixed. Fixed servers cannot adapt to emergency scenarios such as disaster rescue
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