Long range wide area network (LoRaWAN) represents a promising low power wide area network (LPWAN) technology in the context of internet-of-things (IoT) that has recently attracted intense research interest. Due to the limited energy resources available on LoRaWAN constituent elements and intermittent power supply of gateways in harsh environments, an energy-efficient communication protocol is constituted of utmost importance in order to prolong network lifetime. Motivated by the aforementioned, this work presents a green, robust, and resilient communication protocol, namely GreenLoRaWAN, which increases energy efficiency, scalability and robustness of the LoRaWAN. The proposed protocol is evaluated by means of Monte Carlo simulations; Performance evaluation results acquired are very promising, revealing an important reduction in energy consumption and increase the duration of network lifetime.

Long range wide area networks (LoRaWANs) have recently received intense scientific, research, and industrial interest. LoRaWANs play a pivotal role in Internet of Things (IoT) applications due to their capability to offer large coverage without sacrificing the energy efficiency and, thus the battery life, of end-devices. Most published contributions assume that LoRaWAN gateways (GWs) are plugged into the energy grid; thus, neglecting the network lifetime constraint due to power storage limitations. However, there are several verticals, including precision agriculture, forest protection, and others, in which it is difficult or even impossible to connect the GW to the power grid or to perform battery replacement at the end-devices. Consequently, maximizing the networks’ energy efficiency is expected to have a crucial impact on maximizing the network lifetime. Motivated by this, as well as the observation that the overall LoRaWAN network energy efficiency is significantly affected by the selected communication protocol, in this paper, we identify and discuss critical aspects and research challenges involved in the design of a LoRaWAN communication protocol, under an energy efficiency perspective. Building upon our findings, research directions towards a novel GreenLoRaWAN communication protocol are given, focusing on achieving energy efficiency, robustness, and scalability.

Access to clean water is vital to human health, communities’ development, and economic prosperity. Nowadays, more than 10% of the global population lacks access to clean water. This has created the so-called “water crisis” and set, as a key goal for communities, the protection and optimization of water usage. In this direction, technological concepts, like internet-of-things (IoT) and artificial intelligence (AI) assisted recommendation, which enables real-time monitoring and efficient exploitation of water resources, have been identified as fundamental pillars of the solution. This inspired the design, development, and testing of several breakthrough concepts in this domain; however, to the best of our knowledge, none of them lies in real-time intelligent exploitation of mixing clean and recycled water for crops irrigation. Motivated by this, in this paper, we present a holistic next-generation IoT approach, namely AUGEIAS, for optimal clean and treated wastewater usage in precision agriculture. In more detail, we present AUGEIAS architecture and explain its features and functionalities. Moreover, the AUGEIAS intelligent mechanisms that allow accurate crops water demand and weather prediction, as well as optimization, are documented. Finally, the front end of AUGEIAS platform is presented.

This work presents the project AUGEIAS, an intelligent internet of things (IoT) management platform for reuse of treated wastewater in precision agriculture. The AUGEIAS project aims to create an intelligent ecosystem, which enables and optimizes the safe and efficient re-use of wastewater from the output of the wastewater treatment plant (WWTP) for irrigation purposes. Advanced IoT systems, combined with low power wide area networks, cloud computing, data analytics and artificial intelligence technologies are utilized to improve the involved decision-making processes for farmers and utility companies. In this paper, we discuss AUGEIAS, objectives, operational requirements, architecture and the experimental testbed developed for initial project testing and to identify potential issues and improvements.