## 2 Related Research and Motivational Challenges
present a multi-agent-based method for wirelessly acquiring agricultural context-aware information sustained to increase the diversity and flexibility of the employed sensor types. Finally, an earlier work by [PERSON] et al. as in [67] introduced an architectural framework of ubiquitous sensor networks for agriculture and livestock farms involving a server-side context-aware middleware which systematically coordinates interactions between all components involved, improving in this way the reliability and rightness of sensory data. It is worth noting that the models proposed in these contributions are mainly developed as standalone applications, and furthermore none of them succeeds in exceeding the limit of remote data acquisition and transmission. Much more efficient appears to be the scanning in the literature regarding agricultural applications which involve cloud computing and IoT technologies for sustainable rural development, given that considerable works have been identified in this research field during the present decade. Indicatively, [PERSON] et al. in their study as in [68], used available and low-cost technologies, such as GPS cameras and cloud storage, to create a cloud-based spatial-temporal repository system for monitoring phenology and other information related to plants while [PERSON] et al. proposed as in [69] an e-Agriculture application based on a framework which consists of a knowledge management base and monitoring modules in order to connect various distinct sources to the crop structures. Moreover, several researchers, inter alia [PERSON] and [PERSON] [70], [PERSON] et al. [71] as well as [PERSON] and [PERSON] [72], proposed architectural frameworks according to which open-field WSNs or WSANs are combined with cloud computing services for assisting farmers to optimize the usage of available resources in agricultural production operations. Nonetheless such solutions present mostly standalone applications involving microcontrollers' interfaces for commercial devices or sensors which employ online information libraries and schemes. Finally, for ease of reference, some quite recent literature surveys reviewing systematically the IoT technologies penetration in agricultural applications were studied [6, 23, 28, 73, 74, 75, 76, 23]. What most of these reviews have in common is that they conclude to the fact that although the IoT technologies are promptly evolving and providing considerable novel agricultural applications and services, some critical issues concerning the interoperability as well as the semantic annotation of heterogeneous data have to be handled. In this respect, the FIWARE project as in [76], which has been developed within the scope of the Future Internet Public Private Partnership (FI-PPP) launched by the European Commission in collaboration with ICT industries, is considered to be a quite ambitious approach conceived to deliver a framework of open source platform components capable of providing adaptive solutions for internet technology that can be used into various usage domains which include smart agriculture as well [43]. In particular, FIWARE offers various added-value functions (services) by elaborating a public cloud along with an enriched library of modules, referred as Generic Enablers (GEs), which fulfil capabilities such as cloud hosting, IoT, services support and delivery, developer tools as well as network and devices interface [43; 76]. Yet, it has been regarded that issues of interoperability among FIWARE GEs and applications exist while a considerable number of design flaws and severe bugs appear in some of the components as well [77; 78; 79]. Besides that, FIWARE is a rather large and complex platform involving enormous sets of services and components, which seem to be difficult to comprehend probably due to the lack of documentation updates.