## 1 Introduction  
Essentially, structural health monitoring (SHM) is about monitoring infrastructure and providing predictions regarding fatigue life. SHM acts by sending waves from sources ranging from acoustic, thermal, or electromagnetic, among others. These methods comprise different implementations and sensors, and they are, therefore, crucial to be knowledgeable about them [1]. SHM is a major trend as an application in the transport domain, and it includes aircraft state monitoring, high-speed train car-bodies, rail tracks, bridges, etc. [2; 3; 4; 5; 6]. Here, SHM in carbodies is considered for potential cracks or disjoint parts. An excellent method for performing SHM is acoustic emission (AE), among others [7]. Acousto-ultrasions also utilise AE; however, this method constitutes an inspection method [8; 9]. There are a number of applications of AE monitoring in different infrastructure [10]. Importantly, AE can be utilised for real-time damage detection via monitoring. AE is essentially the generation of elastic waves due to the release of energy from a localised source within a material composing a structure. Among the main features of AE one can define are the capability of real-time monitoring, high sensitivity in the sensing, global monitoring, source location, high sensitivity to any mechanism that produces stress waves, and passive nature since the energy from the source is encapsulated [11]. In [12], the authors investigated microseismic/acoustic emission source localisation in order to predict dangerous events on complex structures. The main problem is the location of the errors, which are attributed to irregular structure and pre-measured velocity. A velocity-free- microseismic/AE location method is implemented in order to satisfy the high accuracy location needs in complex three-dimensional hole-containing structures. [PERSON] [13] conducted an unaxial multi-stage loading test on siltstone specimens, in order to examine the staged evolutionary features during multi-stage loading based on wave velocity imaging and AE monitoring. The comparison and analysis of the behaviour during the stages of pressure maintenance and loading, during multistage loading, they discovered the localisation effect during rock failure. Moreover, as read in [14], in situ monitoring for tasks of Additive Manufacturing (AM) can be undertaken using AE methods. SHM using AE could comprise monitoring the entire state of a structure using an array of sensors and assessing their parameters. Moreover, monitoring of a single location in a structure can be conducted, such as the growth of a crack in a specific part of the structure, using one or a plethora of sensors. The acquisition of data from AE sensors is of utmost importance since the data is continuous and the data need to be transferred to a dedicated server. Wireless communications have emerged in the field of the SHM and AE in order to transmit and forward the data to a terminal computer. In particular, wireless sensor networks (WSN)s [15; 16; 17] and their successors, namely the Internet of Things (IoT) [18], have been assigned a vital role in the next generation of SHM. Wireless technologies such as zigbee [19], Wi-Fi [20] and telecommunication solutions [21] transform SHM into a wireless networked process, where enhanced autonomicity is accomplished. Note that, especially for the wireless connectivity, solutions such as the Micro-SHM [22] offer 4G capabilities as well as Wi-Fi. In zigbee and Wi-Fi communications, it may experience interference due to lossy links. SHM with acoustic emissions usually requires a Data Acquisition Board to perform calculations of the continuous signal that comes from the AE sensors. Thereafter, the signal that exceeds a specific threshold may be sent to a terminal computer for analysis of specific features. The use of a field-programmable gate array (FPGA) often performs this task. The transmission of the packets using wireless technologies needs to be optimised, in order to meet the high data rate of the AE.