With the rapid development of unmanned aerial vehicle (UAV), extensive attentions have been paid to UAV-aided data collection in wireless sensor networks. However, it is very challenging to maintain the information freshness of the sensor nodes (SNs) subject to the UAV’s limited energy capacity and/or the large network scale. This chapter introduces two modes of data collection: single and continuous data collection with the aid of UAV, respectively. In the former case, the UAVs are dispatched to gather sensing data from each SN just once according to a preplanned data collection strategy. To keep information fresh, a multistage approach is proposed to find a set of data collection points at which the UAVs hover to collect and the age-optimal flight trajectory of each UAV. In the later case, the UAVs perform data collection continuously and make real-time decisions on the uploading SN and flight direction at each step. A deep reinforcement learning (DRL) framework incorporating the deep Q-network (DQN) algorithm is proposed to find the age-optimal data collection solution subject to the maximum flight velocity and energy capacity of each UAV. Numerical results are presented to show the effectiveness of the proposed methods in different scenarios.

Optimization of information freshness in wireless networks has usually been performed based on queueing analysis that captures only the temporal traffic dynamics associated with the transmitters and receivers. However, the effect of interference, which is mainly dominated by the interferers’ geographic locations, is not well understood. This chapter presents a theoretical framework for the analysis of the Age of Information (AoI) from a joint queueing-geometry perspective. We also provide the design of a decentralized scheduling policy that exploits local observation to make transmission decisions that minimize the AoI. To quantify the performance, we derive analytical expressions for the average AoI. Numerical results validate the accuracy of the analyses as well as the efficacy of the proposed scheme in reducing the AoI.

This chapter characterizes the average Age of Information (AoI) for the case of having multiple sources sharing a service facility with a single server. In particular, a simplified explanation of the SHS for AoI approach is provided to calculate the average age of updates of any source at the monitor. This approach is applied to various queueing systems including FCFS, M/M/1*, and M/M/1/2*, and the latter two with and without source priorities.

This chapter considers an application of age of information called AoCSI in which the channel states in a wireless network represent the information of interest and the goal is to maintain fresh estimates of these channel states at each node in the network. Rather than sampling some underlying time-varying process and propagating updates through a queue or graph, the AoCSI setting obtains direct updates of the channels as a by-product of wireless communication through standard physical layer channel estimation techniques. These CSI estimates are then disseminated through the network to provide global snapshots of the CSI to all of the nodes in the network. What makes the AoCSI setting unique is that disseminating some CSI updates and directly sampling/estimating other CSI occur simultaneously. Moreover, as illustrated in this chapter, there are inherent trade-offs on how much CSI should be disseminated in each transmission to minimize the average or maximum age.

While age of Information (AoI) has gained importance as a metric characterizing the freshness of information in information-update systems and time-critical applications, most previous studies on AoI have been theoretical. In this chapter, we compile a set of recent works reporting AoI measurements in real-life networks and experimental testbeds, and investigating practical issues such assynchronization, the role of various transport layer protocols, congestion control mechanisms, application of machine learning for adaptation to network conditions, and device-related bottlenecks such as limited processing power.

This chapter explores Age of Information (AoI) in the context of the timely source coding problem. In most of the existing literature, service (transmission) times are based on a given distribution. In the timely source coding problem, by using source coding schemes, we design the transmission times of the status updates. We observe that the average age minimization problem is different than the traditional source coding problem, as the average age depends on both the first and the second moments of the codeword lengths. For the age minimization problem, we first consider a greedy source coding scheme where all realizations are encoded. For this source coding scheme, we find the age-optimal real-valued code word lengths. Then, we explore the highest k selective encoding scheme, where instead of encoding all realizations, we encode only the most probable k realizations. For each source encoding scheme, we first determine the average age expressions and then, for a given pmf, characterize the age-optimal k value, and find the corresponding age-optimal codeword lengths. Through numerical results, we show that selective encoding schemes achieve lower average age than encoding all realizations.