Spreading Factor Research Articles

Optimized Low-Powered Wide Area Network within Internet of Things

The Internet of Things (IoT) is rapidly becoming an integral part of everyday life. LPWANs have been introduced to support the billions of internet-connected devices and the data they produce. LPWANs are capable of providing reliable connectivity even in low-density areas and devices consuming a low amount of energy. The exponential increase in the use of IoT applications across the globe will continue to generate more and more data traffic within the IoT network. Hence, it will increase device battery usage that may reduce the battery life expectancy limits. Thus, End Devices (EDs) within the IoT network in the near future will rise up to billions of devices operating in public, industry, and personal networks, generating a necessity for more correct and reliable energy conservation technology. This prompted the research work on an optimized low-powered wide area network within IoT. This paper focuses on three different strategies: LoRa power consumption model design, simulation of IoT wireless sensor networks, and implementation of SF allocation across the wireless sensor network and results analysis. The experiment has been carried out in various stages: firstly running a simulation over a wireless sensor network without optimization using MATLAB Simulink and obtaining the following result of 6.3997e-17 joules power consumption. Secondly, the authors test the network with power optimization using particle swarm optimization algorithms and obtained a better result of 2.5230e-17 joules. The LoRa energy consumption is reduced by 60%. Lastly, different simulation tests of LoRaWAN protocols with respect to throughput, packet loss, delay, data transmission, buffer size, and network density. The results presented on the graph showed that the proposed model outperforms the existing models. Hence, appropriate spreading factor allocation has increased the power efficiency of LoRa end device battery.

Fast spreading of liquid on Leidenfrost vapor layer surface

Previous studies found that a surface with a Leidenfrost vapor layer can effectively decrease the drag of the flow field and even increase the slip length on the boundary flow. This study investigated this phenomenon by the experiment of impact of water droplets on surfaces both with and without Leidenfrost vapor layers in the film boiling regime. The rapid spreading behaviors are dominated by the inertia of the droplet, which mainly depends on the surface with or without Leidenfrost vapor film. In the cases with higher inertia force of critical Weber numbers, this study observed that a surface with a Leidenfrost vapor film can effectively increase the wetting velocity and induce larger maximum spreading factor, especially in the case of rough surfaces.

An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks

LoRa is one of the most popular low-power wireless network technologies for implementation of the Internet of Things, with the advantage of providing long-range communication, but lower data rates, when compared with technologies such as Zigbee or Bluetooth. LoRa is a single-channel physical layer technology on top of which LoRaWAN implements a more complex multi-channel network with enhanced functionalities, such as adaptive data rate. However, LoRaWAN relies on expensive hardware to support these functionalities. This paper proposes a LoRa data-link-layer architecture based on a multi-layer star network topology that adapts relevant LoRa parameters for each end node dynamically taking into account its link distance and quality in order to balance communication range and energy consumption. The developed solution is comprised of multiple components, including a LoRa parameter calculator to help the user to configure the network parameters, a contention-free MAC protocol to avoid collisions, and an adaptive spreading factor and transmission power mechanism. These components work together to ensure a more efficient use of the chosen ISM band and end node resources, but with low-cost implementation and operation requirements.

Energy efficient selection of spreading factor in LoRaWAN-based WBAN medical systems

Recently, the Internet of Things has been leading the technological revolution in several fields and applications. This revolution affected healthcare services adding more value to the concept of e-health. To improve the quality of these services, remote monitoring allows healthcare professionals to observe and diagnose their patients without being physically present. Since the spreading factor affects the energy consumption, the data rate, the receiver sensitivity and the time on air. A monitoring healthcare system using LoRa technology is proposed on this work to adopt the data transmission in a reliable and energy-efficient way. We propose a method to select the most convenient spreading factor based on the patients medical state. This method takes in consideration the distance to gateway, the packet error rate and the energy consumption requirements.We first determine the criticality level of the patient using the Early Warning Score system. Second, a decision about the optimal spreading factor is taken based on this level. We use a method based on the technique for order preference by similarity to an ideal solution to select a spreading factor. We use real data to show that the results reflect an acceptable increase in energy consumption while applying our selection method. In contrast, the results show that the selection method reduces the error rate especially for patients with a high critical level.

Effect of iso-propanol additive on the impact dynamics of a Leidenfrost water droplet

This paper experimentally investigated the Leidenfrost dynamics of an impacting droplet of water and 2–8 vol% iso-propanol-water solutions in the film boiling regime up to 450 °C. A single millimetric droplet was released from a height of several centimeters to yield different impact velocities. The aim of the present study was to clarify the influence of iso-propanol additive on the dynamic Leidenfrost point temperature, maximum spreading factor and nondimensional residence time, which remains to be verified. Visualization study with high-speed imaging indicated that the droplet impact pattern depends on both surface temperature and iso-propanol additive. It was shown that the dynamic Leidenfrost point temperature increases with impact velocity, and was significantly elevated with a small amount of iso-propanol additive by approximately 40 °C at the same Weber number. Especially, the lowest value of the mixture droplet with the smallest impact momentum (430 °C at 0.44 m/s and We = 6.2–8.3) still surpassed the highest value of the water droplet with the largest impact momentum (410 °C at 1.33 m/s and We = 55.0) within the present test range. Meanwhile, the iso-propanol additive markedly enhanced the droplet spreading ability, which was speculated to be caused by lower surface energy. Results also demonstrated that the nondimensional residence time was highly dependent on the impact velocity, but was not significantly influenced by the iso-propanol additive. Empirical correlations of the maximum spreading factor and nondimensional residence time were proposed with satisfactory predictive accuracy and good universality.

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning.

The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network.

An experimental study on the impact of Boger and Newtonian droplets on spherical surfaces

This study examined the impact of Boger droplets on spherical surfaces, considering the various properties of liquids and surfaces for the first time. The experimental setup involved spheres with three different diameters composed of hydrophilic and hydrophobic surfaces. The main innovative aspect of the present study lies in the examination of the effects of rheological properties including elasticity, elongational viscosity, and the first normal stress difference on the impact of Boger droplets on solid surfaces via stress analysis. For this purpose, the results of the impact of Boger droplets are compared to the impact of equivalent Newtonian droplets with the same viscosity and surface tension coefficient. The research also explored the influences of Weber number, diameter ratio, and surface wettability on the dynamic behaviors of viscoelastic droplets during the spreading and receding stages. The results suggested that increasing the Weber number increases the maximum spreading factor of the droplets. A smaller diameter ratio required more time to reach this factor. The surface wettability significantly affected the receding phase, while its effect on the spreading stage was minimal. Droplets exhibited more retraction on surfaces with higher contact angles. The Newtonian fluids showed greater spreading and retraction than viscoelastic fluids with the same viscosity, highlighting the influence of fluid elasticity. It is shown that the normal stress caused by extensional viscosity during droplet spreading is significantly higher than the normal stress difference resulting from shear deformation, showing different behaviors between these two types of stress concerning droplets.

Water-in-oil emulsion drop-wall impact: Effect of viscosity and water particles on slip length over an oleophilic surface

The study is focused on the numerical simulation of the boundary slip of a water-in-oil (W/O) emulsion along an oleophilic surface in a 2D axisymmetric formulation of the Navier–Stokes equations for an incompressible laminar flow. Drops of emulsions with a volume fraction of the dispersed phase of 0.04–0.34 collide with a smooth glass wall at initial velocities of 1.2–3.6 m/s; the motion of the interface between the liquid and gas phases is resolved by the level set method. The influence of the wall wettability by oily liquids is demonstrated by varying the static contact angle up to 0.5° and by considering the dynamic one as a function of the empirically measured contact line velocity of the liquid drop according to Tanner's law. Numerical simulation of emulsion drop spreading dynamics reveals that the boundary slip of a heterogeneous liquid along a smooth oleophilic wall is different from that of a homogeneous. The temporal changes in the average viscous stress allowed the illustration of the interrelated effects of emulsion viscosity and the presence of disperse phase particles on the boundary slip of a heterogeneous liquid and its motion in the viscous boundary layer. The analytical expression evaluating the slip length for an arbitrary W/O emulsion was derived using the effective properties of the liquid. In testing the expression, the values of the maximum spreading factor of drops of an arbitrary W/O emulsion predicted by numerical simulation correspond to those determined from experiments at a moderate error of 7.4%.

Combinatorial MAB-Based Joint Channel and Spreading Factor Selection for LoRa Devices.

Long-Range (LoRa) devices have been deployed in many Internet of Things (IoT) applications due to their ability to communicate over long distances with low power consumption. The scalability and communication performance of the LoRa systems are highly dependent on the spreading factor (SF) and channel allocations. In particular, it is important to set the SF appropriately according to the distance between the LoRa device and the gateway since the signal reception sensitivity and bit rate depend on the used SF, which are in a trade-off relationship. In addition, considering the surge in the number of LoRa devices recently, the scalability of LoRa systems is also greatly affected by the channels that the LoRa devices use for communications. It was demonstrated that the lightweight decentralized learning-based joint channel and SF-selection methods can make appropriate decisions with low computational complexity and power consumption in our previous study. However, the effect of the location situation of the LoRa devices on the communication performance in a practical larger-scale LoRa system has not been studied. Hence, to clarify the effect of the location situation of the LoRa devices on the communication performance in LoRa systems, in this paper, we implemented and evaluated the learning-based joint channel and SF-selection methods in a practical LoRa system. In the learning-based methods, the channel and SF are decided only based on the ACKnowledge information. The learning methods evaluated in this paper were the Tug of War dynamics, Upper Confidence Bound 1, and ϵ-greedy algorithms. Moreover, to consider the relevance of the channel and SF, we propose a combinational multi-armed bandit-based joint channel and SF-selection method. Compared with the independent methods, the combinations of the channel and SF are set as arms. Conversely, the SF and channel are set as independent arms in the independent methods that are evaluated in our previous work. From the experimental results, we can see the following points. First, the combinatorial methods can achieve a higher frame success rate and fairness than the independent methods. In addition, the FSR can be improved by joint channel and SF selection compared to SF selection only. Moreover, the channel and SF selection dependents on the location situation to a great extent.

Effects of solution concentration and surface supercooling on the spreading characteristics of an impact binary droplet

We investigate the effect of liquid properties and surface supercooling on the spreading dynamics of an impact binary ethylene-glycol and ethanol droplet. Surface supercooling and solution concentration, affecting the physical properties of a droplet, jointly determine the maximum spreading factor. At a high concentration of ethylene-glycol, the maximum spreading factor decreases with surface supercooling. At a low concentration of ethylene-glycol, the maximum spreading factor decreases and then increases with surface supercooling. The relation between the maximum spreading factor and surface supercooling is resulting from the opposite surface-supercooling-dependencies of the maximum internal spreading factor and the dimensionless fingering length. The maximum spreading factor decreases with the ethylene-glycol concentration due to increased liquid surface tension and viscosity. A new model of the maximum spreading factor, taking into account the effect of surface cooling in viscous dissipation, is proposed based on energy conservation, which optimizes the prediction of spreading of an impact droplet on a surface with different supercooling degrees.

On the Quasi-Orthogonality of LoRa Modulation

LoRa (Long Range), a low power and wide area modulation scheme based on chirp spread spectrum, is the most popular and widely adopted Internet-of-Things (IoT) technique in industry. A notable and interesting property of LoRa modulation is the quasi-orthogonality of signals modulated under different spreading factors (SFs). Unfortunately, in the literature, there has been no analytical effort to establish the theoretical validity of such quasi-orthogonality. This paper, for the first time, theoretically tackles the quasi-orthogonality of the LoRa modulation. First, we derive in both continuous-and discrete-time domains the cross-correlation between two non-synchronized LoRa signals with different SFs, based on which we analyze the quasi-orthogonality of the LoRa modulation and draw some useful engineering insights. Particularly, we analytically show that in the continuous-time domain, the quasi-orthogonality is guaranteed if one of the SFs of the two LoRa signals is large enough; while, in the discrete-time domain, the quasi-orthogonality is ensured if the maximum of the SFs is large enough. Furthermore, for practical values of the SF, the maximum squared magnitudes of the cross-correlation in the continuous-and discrete-time domains are shown to be 1.14% and 1.08%, respectively, compared to their peak values. We demonstrate the validity and accuracy of our analysis through extensive numerical simulations.

Molecular Dynamics Simulation of Droplet Impact on a Hydrophobic 3D Elastic Surface.

The phenomenon of droplets impacting elastic surfaces is common in nature and in many engineering applications. It has been shown that droplet impact on an elastic surface drastically reduces droplet contact time and hinders droplet spreading. However, most of the current studies are based on experiments, and the analysis of the influence mechanism of the elastic substrate on the dynamic behavior of droplets is not complete. In addition, the simulations of droplet impact on elastic substrates are mainly focused on 2D elastic films or vibrating rigid substrates, ignoring the effect of 3D elastic substrate deformation on the droplet dynamic behavior. Therefore, in this paper, we propose to model the droplet impact on a 3D hydrophobic elastic substrate using the molecular dynamics method. We find that droplet pancake rebound can substantially reduce the droplet contact time. Moreover, we record the conditions required for the pancake rebound of the droplet. Furthermore, we investigated the effects of the elastic modulus of the substrate and the initial velocity of the droplet on the droplet contact time, contact area, and spreading factor. This study further elucidates the influence mechanism of the elastic substrate on the dynamic behavior of the droplet and provides theoretical guidance for regulating the dynamic behavior of the droplet in related fields.

Experimental study on the droplet dynamics after impacting an inclined superhydrophobic surface

Super-hydrophobic surface has been widely studied, related to surface self-cleaning and liquid transport. In this study, effects of the inclined angle and impact velocity on the dynamic characteristics of droplet impact are experimentally investigated, using high-speed photography. When the spreading break-up does not occur, the effect of the inclined angle on the maximum spreading diameter is mainly determined by the competition between the decrease in the inertial force component normal to the surface and the increase in the gravity and inertial force component parallel to the surface. A formula has been developed to predict the maximum spreading factor under different inclined angles. The increasing inclined angle promotes the complete rebound. When the impact velocity is constant, there exists a critical inclined angle, at which the rebounding time is minimum. Furthermore, the critical inclined angle increases with the impact velocity. The increasing inclined angle can affect droplet ejection and potentially hinder its occurrence. Droplet ejection is classified into five stages based on the change in the ratio of the ejected to initial droplet diameter versus Weber number normal to the surface. For stage 5, an empirical formula that predicts the diameter of ejected droplets under different inclined angles is given out. This study can provide a novel idea for the generation and transport of microdroplets of directed size, as well as the recycling of the remaining liquid film after droplet ejection.

Intelligent Drone Positioning via BIC Optimization for Maximizing LPWAN Coverage and Capacity in Suburban Amazon Environments.

This paper aims to provide a metaheuristic approach to drone array optimization applied to coverage area maximization of wireless communication systems, with unmanned aerial vehicle (UAV) base stations, in the context of suburban, lightly to densely wooded environments present in cities of the Amazon region. For this purpose, a low-power wireless area network (LPWAN) was analyzed and applied. LPWAN are systems designed to work with low data rates but keep, or even enhance, the extensive area coverage provided by high-powered networks. The type of LPWAN chosen is LoRa, which operates at an unlicensed spectrum of 915 MHz and requires users to connect to gateways in order to relay information to a central server; in this case, each drone in the array has a LoRa module installed to serve as a non-fixated gateway. In order to classify and optimize the best positioning for the UAVs in the array, three concomitant bioinspired computing (BIC) methods were chosen: cuckoo search (CS), flower pollination algorithm (FPA), and genetic algorithm (GA). Positioning optimization results are then simulated and presented via MATLAB for a high-range IoT-LoRa network. An empirically adjusted propagation model with measurements carried out on a university campus was developed to obtain a propagation model in forested environments for LoRa spreading factors (SF) of 8, 9, 10, and 11. Finally, a comparison was drawn between drone positioning simulation results for a theoretical propagation model for UAVs and the model found by the measurements.

A Resilient LoRa-Based Solution to Support Pervasive Sensing

Today, billions of small devices that can sense things are connected, creating the Internet of Things (IoT). This major technological step has led to ideas like smart cities, smart factories, and smart countries. One important use of this technology is pervasive sensing, which could benefit from a network that covers a wide area but does not use much power. This paper looks closely at the advantages and disadvantages of using LoRa—a network technology that can reach long distances with limited energy use—in situations like this. To this aim, we have created a holistic solution to manage the considered network enabling synchronization, routing, and reliability. In particular, we have even developed an adaptive spreading factor mechanism, simple and effective in allowing the network to cope better when the connection is not very good.

A New Index Modulation for LoRa

LoRa (Long Range) is a widely adopted Internet-of-Things (IoT) technique, but its fatal limit is a low data rate. In this letter, we propose a new index modulation for LoRa to increase the data rate, in which multiple quasi-orthogonal chirps modulated under different spreading factors (SFs) are concurrently transmitted, thereby leveraging the SF domain as a means to carry more information. Both coherent and non-coherent detection algorithms for the proposed index modulation are also developed in efficient forms. Numerical results demonstrate that the proposed scheme notably outperforms the state-of-the-art techniques.

Machine learning‐based LoRa localisation using multiple received signal features

AbstractLow‐power localisation systems are crucial for machine‐to‐machine communication technologies. This article investigates LoRa technology for localisation using multiple features of the received signal, such as Received Signal Strength Indicator (RSSI), Spreading Factors (SF), and Signal to Noise Ratio (SNR). A novel range‐based technique to estimate the distance of a target node from a LoRa gateway using machine‐learning models that incorporates SF, SNR, and RSSI to train the models is proposed. A modified trilateration approach is then used to localise the target node from three gateways. Our experiment used three LoRaWAN gateways and two sensor nodes, on a sports oval with an approximate area coverage of 30,000 square metres. The authors also used a public LoRaWAN dataset to build a model test the proposed method and compare both range‐based distance mapping with trilateration and fingerprint‐based direct location estimation techniques. Our method achieved an average distance error of 43.97 m on our experimental dataset. The results show that the combination of RSSI, SNR, and SF‐based distance mapping provides ∼10% improvement on ranging accuracy and 26.58% higher accuracy for trilateration‐based localisation when compared with just using RSSI. Our method also achieved 50% superior localisation accuracy with fingerprint‐based direct location estimation approaches.

Dynamic Parameter Allocation With Reinforcement Learning for LoRaWAN

LoRaWAN attracted lots of attention with its capacity for large device numbers, long-range and low power consumption. In order to simplify the transmission procedure, a pure Aloha protocol is implemented into its MAC layer. However, as the number of connected devices to the base station increases, the devices’ transmission parameters allocation becomes a vital issue related to network performance. This research contributes to the decentralized dynamic Spreading Factor (SF) allocation strategies during transmission by proposing STEPS, a Score Table based Evaluation and Parameters Surfing approach. STEPS is a reinforcement learning-based method that evaluates and changes the parameters based on probability and score tables. It provides a nondeterministic parameter selection method by updating the table while transmitting. Some variants of STEPS with different algorithms are proposed. Moreover, an estimation-based initialization is proposed to improve learning performance. Simulations and statistical tests are carried out with MULANE, a lightweight LoRaWAN Simulator developed in our previous work. The results show that the estimation has a high confidence level. Compared with the baseline methods, the proposed methods reduce energy consumption by 24-27% in different numbers of nodes. For bi-directional transmission, the proposed methods increase the 18% network throughput in a small number of nodes and 33% in a large number of nodes. Moreover, the proposed methods provide a framework of decentralized parameter allocation, which gives the extendability of this work.

Machine-Learning-Based Combined Path Loss and Shadowing Model in LoRaWAN for Energy Efficiency Enhancement

Many practical Internet of Things (IoT) applications require deploying End Nodes (ENs) in hard-to-access places where replacing batteries is difficult or impossible. As a result, the ENs demand high energy efficiency. Long Range Wide Area Network (LoRaWAN) is an IoT protocol that aims to achieve low energy consumption. However, the energy consumption in LoRaWAN is related to transmission power, which can be set mainly based on path loss and shadow fading modeling and link budget analysis. Hence, appropriately setting this transmission power parameter saves energy and guarantees reliable communication links. Traditional path loss and shadow fading modeling and transmission power setting do not consider the variations caused by different environmental effects. In this work, we show via real-life data analysis that path loss and shadow fading depend on environmental variables. We propose Machine Learning models to calculate the empirical path loss and shadow fading, which is used to set the transmission power to save ENs’ energy. Our models include the effects of distance, frequency, temperature, relative humidity, barometric pressure, particulate matter, and Signal to Noise Ratio. Specifically, the models are based on Multiple Linear Regression, Support Vector Regression, Random Forests, and Artificial Neural Networks, exhibiting a Root Mean Square Error (RMSE) up to 1.566 dB and R up to 0.94. For energy saving, the developed models serve to set the transmission power and Spreading Factor based on the Adaptative Data Rate (ADR) algorithm principles, which reduces the link margin saving energy up to 43% compared with the traditional ADR protocol.

Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways.

LoRaWAN has imposed itself as a promising and suitable technology for massive machine-type communications. With the acceleration of deployment, improving the energy efficiency of LoRaWAN networks has become paramount, especially with the limitations of throughput and battery resources. However, LoRaWAN suffers from the Aloha access scheme, which leads to a high probability of collision at large scales, especially in dense environments such as cities. In this paper, we propose EE-LoRa, an algorithm to improve the energy efficiency of LoRaWAN networks with multiple gateways via spreading factor selection and power control. We proceed in two steps, where we first optimize the energy efficiency of the network, defined as the ratio between the throughput and consumed energy. Solving this problem involves determining the optimal node distribution among different spreading factors. Then, in the second step, power control is applied to minimize the transmission power at nodes without jeopardizing the reliability of communications. The simulation results show that our proposed algorithm greatly improves the energy efficiency of LoRaWAN networks compared to legacy LoRaWAN and relevant state-of-the-art algorithms.