## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures  
### AoI Minimization  
The critical applications of UAV-assisted IoT emphasize the importance of timely data delivery by the UAV, especially in disaster and time-sensitive monitoring applications. This is particularly because timely sensed information can make an accurate and informed decision. Untimely updates may not correctly capture the current state of the data being sensed, which may lead to wrong decisions being made. It becomes even more concerning considering IoT devices have constraints, such as relatively short transmission range and low onboard energy, despite the need for reliable communication to the final destination. This motivates the consideration of techniques for timely information delivery [9], such as the use of UAVs for relaying data sensed by sensors and IoTDs, which led to the study of the AoI metric on the UAV-assisted WSN/IoT architecture. The first research study that introduced the concept of AoI can be traced back to the year 2012 in [107], which focused on real-time status updates.  
AoI is defined as the amount of time that elapses after the generation of the most recent update before it is received at the destination. AoI characterizes the inter-delivery time arrivals and latency [8]. It differs from the delay metric, i.e., the time that was spent on data transmission from the source to the destination [2]. Applications where status information needs to be delivered to the destination quickly for online data analysis and decision-making, such as environmental monitoring, health monitoring, and safety protection, can be characterized using AoI [85]. UAVs are favorable in terms of their speed, flexibility, and dynamism, thus they attract researchers' attention, particularly in AoI-aware WSN and IoT architecture design. For UAV-based data gathering, the AoI of an arbitrary physical process is defined as the time elapsed since the most recently received update packet at the UAV was generated at the ground node [4].  
Figure 4: Categorization of design aspects of AoI minimization in UAV-assisted WSN/IoT networks based on [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 86; 89; 91; 92; 105; 106].  
Numerous researchers have studied the objective of AoI minimization using different target metrics. For example, normalized weighted sum AoI (NWAoI) was considered in [4], AoI cost in [87], maximum AoI (max. AoI) in [1; 79; 84; 89; 91], and meanwhile, average AoI was investigated in [79; 85; 89; 92; 106]. In [85], the authors specifically studied minimum average AoI. The sum of AoI was used in the work of [9; 81], and this metric has been further extended to the weighted sum of AoI in [3; 8; 80]. The authors in [1] formulated weighted sum expected AoI. Expected sum AoI (ESA) was studied in [9], the authors of [90] evaluated peak AoI, while ave peak AoI was considered in [82]. Finally, the general AoI performance metric was investigated in [2; 5; 6; 7; 83; 86; 93; 98; 99; 101; 102; 103; 105].