## 1 Introduction
Reliable soil measurements require long and often unattended periods of data collection, which is translated to extended exposure of the IoT nodes to drastically varying external environment conditions (e.g., soil radiation, wind, temperature, and humidity variations). Similarly, lightning and thunder nearby may occur, vegetation can alter in density and height, and the presence of animals and/or unauthorized/malicious persons in the area of interest cannot always be avoided. The need for improved energy autonomy increases complexity, as it presupposes that the participating components will enter a low-power (sleep) state when not in action for elongated battery life. The cost of data collection, processing, transmission, and storage, especially of commercial out-of-the-box solutions, can be quite demanding, requiring expensive digitizers, subscriptions to proprietary communication links even per sensor unit, and power-hungry elements. A typical example are widely used cellular networks. As multidisciplinary is not always among the strong points of scientists [13; 14], they are often unwilling to delve into the details of the assistive modules they need and/or to cooperate effectively with persons of more technical specialties. Hence, using data harvesting solutions utilizing more conventional communication technology and equipment offered by the manufactures of the sensing instruments often seems a safe option. Unfortunately, the latter option is also pricy and/or may not incorporate the latest technological outcomes. On the other hand, trying to demystify the role of the underlying technical functions and the feasibility of using third-party and less expensive components is not without flaws, as several low-cost solution alternatives presented in the research community utilize components that are quite unsuitable for low-power operation due to their high consumption or to their lack of sleep function, such as Raspberry Pi units [15]. But even in cases when a sleep function is an option, it is not always exploited by developers or supported efficiently by the accompanying hardware. Indeed, while the main microcontroller chip may have good specifications, the cheap off-the-shelf board hosting it may incorporate components that have much inferior characteristics, such as low-dropout regulators (LDOs) with increased quiescent current of the order of a few mA, which is too high to sustain the sleep mode function efficiently. Another important issue is the heterogeneity of the methods required for data extraction from the sensors of interest (e.g., from the soil moisture/conductivity measuring instruments), which is implied by their diverse operating characteristics. The latter problem, as it is essential for understanding the objectives of the data harvesting solution being presented, is further analyzed in Sections 2.1 and 2.2. The above demanding tasks should be taken under consideration for successful and long-lasting operation, thus indicating specific design and implementation directions for the IoT platform and requiring thorough verification experiments. Successful outcomes can be achieved toward this direction, drawing inspiration from the example presented in [16]. Implementation and installation complexity, costs, security, and privacy requirements and concerns should be addressed to a satisfactory extent [7; 17], i.e., finding a good balance between the contradicting trends for an efficient IoT system operation, especially as farmers exhibit increased skepticism about these issues [18]. It should be noted that among the major priorities of this work is exactly the goal of shedding light on methods to overcome the aforementioned challenges toward the achievement of a cost-effective IoT system able to operate efficiently for a long period and communicating the importance of having both scientists and technicians mutually aware of the problems and solutions dominating their areas of expertise.