A neural network propagation model for LoRaWAN and critical analysis with real-world measurements

Among the many technologies competing for the Internet of Things (IoT), one of the most promising and fast-growing technologies in this landscape is the Low-Power Wide-Area Network (LPWAN). Coverage of LoRa, one of the main IoT LPWAN technologies, has previously been studied for outdoor environments. However, this article focuses on end-to-end propagation in an outdoor–indoor scenario. This article will investigate how the reported and documented outdoor metrics are interpreted for an indoor environment. Furthermore, to facilitate network planning and coverage prediction, a novel hybrid propagation estimation method has been developed and examined. This hybrid model is comprised of an artificial neural network (ANN) and an optimized Multi-Wall Model (MWM). Subsequently, real-world measurements were collected and compared against different propagation models. For benchmarking, log-distance and COST231 models were used due to their simplicity. It was observed and concluded that: (a) the propagation of the LoRa Wide-Area Network (LoRaWAN) is limited to a much shorter range in this investigated environment compared with outdoor reports; (b) log-distance and COST231 models do not yield an accurate estimate of propagation characteristics for outdoor–indoor scenarios; (c) this lack of accuracy can be addressed by adjusting the COST231 model, to account for the outdoor propagation; (d) a feedforward neural network combined with a COST231 model improves the accuracy of the predictions. This work demonstrates practical results and provides an insight into the LoRaWAN’s propagation in similar scenarios. This could facilitate network planning for outdoor–indoor environments

An adaptive neuro-fuzzy propagation model for LoRaWAN

This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios

Characterization of callase (β-1,3-d-glucanase) activity during microsporogenesis in the sterile anthers of Allium sativum L. and the fertile anthers of A. atropurpureum

We examined callase activity in anthers of sterile Allium sativum (garlic) and fertile Allium atropurpureum. In A. sativum, a species that produces sterile pollen and propagates only vegetatively, callase was extracted from the thick walls of A. sativum microspore tetrads exhibited maximum activity at pH 4.8, and the corresponding in vivo values ranged from 4.5 to 5.0. Once microspores were released, in vitro callase activity peaked at three distinct pH values, reflecting the presence of three callase isoforms. One isoform, which was previously identified in the tetrad stage, displayed maximum activity at pH 4.8, and the remaining two isoforms, which were novel, were most active at pH 6.0 and 7.3. The corresponding in vivo values ranged from pH 4.75 to 6.0. In contrast, in A. atropurpureum, a sexually propagating species, three callase isoforms, active at pH 4.8–5.2, 6.1, and 7.3, were identified in samples of microsporangia that had released their microspores. The corresponding in vivo value for this plant was 5.9. The callose wall persists around A. sativum meiotic cells, whereas only one callase isoform, with an optimum activity of pH 4.8, is active in the acidic environment of the microsporangium. However, this isoform is degraded when the pH rises to 6.0 and two other callase isoforms, maximally active at pH 6.0 and 7.3, appear. Thus, factors that alter the pH of the microsporangium may indirectly affect the male gametophyte development by modulating the activity of callase and thereby regulating the degradation of the callose wall

Model for the 3D location of buried assets based on RFID technology

As construction and renovation equipment excavate in the vicinity of utility lines, the buried infrastructure is likely to suffer some form of “attack,” be that simple mechanical abrasion or a major rupture. Damage to underground services can lead to widespread disruption and significant upstream (service provider) and downstream (end user) losses, often resulting in whole communities being isolated from emergency services and losing essential utilities such as water, gas, and electricity. The ability to physically determine on site the location of underground utilities is critical to reduce risk and consequence during excavation.</p