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.

In this work, we use developmental rate models to predict egg laying activity and succession of generations of T. urticae populations under field conditions in the Prespa lakes region in Northern Greece. Moreover, the developmental rate model predictions are related to accumulated heat summations to be compared with actual bean damage and to generate pest-specific degree-day risk thresholds. The oviposition was predicted to start at 57.7 DD, while the first peak in egg laying was estimated to be at 141.8 DD. The second and third peak in egg production were predicted to occur at 321.1 and 470.5 DD, respectively. At the degree-day risk threshold, half development of the first summer generation was estimated at 187 DD and 234 DDm while for the second, it was estimated at 505 DD and 547 DD for 2021 and 2022, respectively. According to the model predictions, no significant differences were observed in the mean generation time (total egg to adult development) of T. urticae between the two observation years (t = 0.01, df = 15, p = 0.992). The total generation time was estimated at 249.3 (±7.7) and 249.2 (±6.7), for 2021 and 2022, respectively. The current models will contribute towards predictions of the seasonal occurrence and oviposition of T. urticae to be used in pest management decision-making. Moreover, the development of population model is a prerequisite for the buildup and implementation of smart plant protection solutions.

The development of temperature-driven pest risk thresholds is a prerequisite for the buildup and implementation of smart plant protection solutions. However, the challenge is to convert short and abrupt phenology data with limited distributional information into ecological relevant information. In this work, we present a novel approach to analyze phenology data based on non-parametric Bayesian methods and develop degree-day (DD) risk thresholds for Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) to be used in a decision support system for dry bean (Phaseolus vulgaris L.) production. The replication of each Bayesian bootstrap generates a posterior probability for each sampling set by considering that the prior unknown distribution of pest phenology is Dirichlet distribution. We computed R = 10,000 temperature-driven pest phenology replicates, to estimate the 2.5%, 50% and 95.5% percentiles (PC) of each flight generation peak in terms of heat summations. The related DD thresholds were: 114.04 (PC 2.5%) 131.8 (PC 50%) and 150.9 (PC 95.5%) for the first, 525.8 (PC 2.5%), 551.7 (PC 50%) and 577.6 (PC 95.5%) for the second and 992.7 (PC 2.5%), 1021.5 (PC 50%) and 1050 (PC 95.5%) for the third flight, respectively. The thresholds were evaluated by estimating the posterior differences between the predicted (2021) and observed (2022) phenology metrics and are in most cases in acceptable levels. The bootstrapped Bayesian risk thresholds have the advantage to be used in modeling short and noisy data sets providing tailored pest forecast without any parametric assumptions. In a second step the above thresholds were integrated to a sub-module of a digital weather-driven real time decision support system for precise pest management for dry bean crops. The system consists of a customized cloud based telemetric meteorological network, established over the border area of the Prespa National Park in Northern Greece, and delivers real time data and pest specific forecast to the end user.

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.