## 1 Introduction
In general, such a node can be appropriately software configured to operate either as end-device, router, coordinator, or, with some extra modifications, as coordinator with gateway functionality. The role configuration is dictated by the networking protocol residing into the MCU's program memory, in the form of a firmware stack. Moreover, there is a power source, commonly not implemented entirely on-board in order to cover the energy needs of the system's components. Regarding the hardware design of the nodes, several realizations that have been introduced in the last fifteen years are strongly influenced by the doctrine of Smart Dust concept (miniaturization, etc.) [6; 26]. The majority of them follow the typical architecture (Figure 1). In practice, all of these nodes enabled the research of WSAN systems in agriculture, because they were mainly used as the development tools, on the basis of which several conglomerate WSAN studies were conducted in the field. Figure 1: Typical architecture of a WSAN node. However, the solutions based on the traditional architecture are under question about their effectiveness in real-world WSAN implementations in agriculture, especially, in the light of the continuously changing technological environment, which is influencing this particular domain. From its infancy, WSAN has been a multi-facet technology. Although WSAN hardware solutions have not been ready enough, in order to accommodate the present requirements of real-life applications, new WSAN applications raise new limits for the hardware systems [27]. Indicatively, wireless multimedia networks (WMNs) [28; 29; 30]; sensor clouds [7]; integration of WSAN and radio frequency identification (RFID) systems [31]; collaboration of WSAN and satellite technology [32]; internet of things (IoT) [33]; and so on, are only some of the trends in modern WSAN applications. Further, with regard to the purely technological aspects of this changing environment, there has been an explosion of new challenging technologies that potentially could help towards the design of more reliable, robust and efficient WSAN systems. Among the most interesting of such technologies are energy harvesting and power management [34]; new networking protocols, such as the IEEE 802.15.4x, LoRa-Net, 6 LoWPAN [35; 36], DASH7 [37], etc. ; big data and data fusion [13]; new human-machine-interfaces (HMI) powered by smart phones and portable computing and connectivity [9]; new micro-electromechanical sensors (MEMS) and advanced sensory concepts such as the Lab-on-Chip (LoC) [38]; new embedded processors such as the low energy and high performance ARM microcontrollers [39; 40]; and, new energy storage media such as lithium-ion-based batteries and hybrid ultra-capacitors [41; 42]. Nowadays, the WSAN hardware solutions, as described in [6], are classified into three classes, namely end-to-end solutions, generic solutions, and research solutions. Furthermore, according to the same study, there are three different WSAN nodes design spaces: the network space, the device space and the application space. Both the preceding and the contemporary nodes are far from meeting all the requirements of real-world applications. In order to ensure the success of WSAN implementations, there must be a close collaboration among the stakeholders, with the focus being on the application requirements. On the other hand, whatever the approach of the design space for WSAN hardware is, there are deficiencies in architectures openness, expandability, and configurability as well as complexities in implementation. As reported by many experts [3; 6], in parallel to the lack of effective hardware design architectures, an additional significant barrier to successful systems development is the very long learning curves associated with several different aspects of the WSAN technology. This is the reason why a mix of multi-blended skills is necessary in research teams.