Spreading Factor Research Articles

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.

Scaling laws for the contact time of impacting nanodroplets: From hydrophobic to superhydrophobic surfaces

Nanodroplet impacts have attracted significant attention, while the effect of surface wettability on contact time is evaluated poorly. Utilizing molecular dynamics simulations, the current work with a special focus on the contact time studies nanodroplets impacting solid surfaces in a wide range of static contact angles (θ0 = 105°–175°) and the Weber number (We = 0.1–200). The complete trends in contact time and restitution coefficient with surface wettability are analyzed and reported for the first time. For surfaces with θ0 > 160°, four different regimes are identified for the contact time and restitution coefficient as a function of the Weber number. For surfaces with 110° < θ0 < 160°, the fourth regime is not observed. The restitution coefficient is employed to analyze the contact time of distinct rebound patterns in the individual wettability range. Intriguingly, surface wettability has a remarkable influence on the contact time of nanodroplets even for superhydrophobic surfaces. The main reason for the difference between the macroscale and nanoscale is attributed to the significantly enhanced viscous effect and interfacial effect of the nanoscale impact. Considering the different effects of surface wettability on spreading and retraction dynamics, the theoretical models for the maximum spreading factor, spreading velocity, and retraction velocity are established. Finally, scaling laws of the spreading time τspr ∼ (R0/Vi)We2/3Re−1/3 and retraction time τret ∼ (R0/Vi)We2/3Re−1/3(1 − cos θ0)−1/2 are proposed. An excellent agreement with both the current data sources and the results in the literature verifies the universality of the current scaling law from hydrophobic to superhydrophobic surfaces.

Contact time of nanodroplets obliquely impacting nanopillar-arrayed superhydrophobic surfaces: A molecular dynamics study

On nanopillar-arrayed superhydrophobic surfaces, the contact time of oblique nanodroplet impacts is for the first time investigated via molecular dynamics simulations. Here, oblique nanodroplet impacts are triggered by nanodroplets impacting superhydrophobic surfaces under various impact angles, α. The simulation results show that owing to the non-axisymmetry of spreading factors on nanopillar-arrayed superhydrophobic surfaces, the contact time of oblique nanodroplet impacts is always less on nanopillar-arrayed rather than smooth superhydrophobic surfaces under same impact angles. As the impact angle is increased from 5° to 65°, that is, under different impact conditions, the non-axisymmetry is more remarkable as α > 35° instead of α < 35° at the low, medium, and high normal Weber numbers, Wen. Hence, the contact time is sharp as α > 35° and then slowly reduced as α < 35° at the low, medium, and high Wen, at which the drastically increased sliding length as α > 35° further promotes the rapid reduction in contact time. As the impact angle is constant at 35°, the non-axisymmetry is more remarkable as h/w < 1 instead of h/w > 1 as the aspect ratio of nanopillars, h/w, is increased, that is, under different surface conditions. Hence, the contact time is sharp as h/w < 1 and then slowly reduced as h/w > 1 at the low, medium, and high Wen, at which the drastically reduced sliding length as h/w > 1 further hinders the rapid reduction in contact time.

A numerical simulation of a droplet impacting a small superhydrophobic cylinder eccentrically

Droplet collisions on superhydrophobic cylindrical surfaces are widely seen in industrial applications. To investigate their dynamic behavior, numerical simulations of droplets impacting eccentrically on the surface of a small superhydrophobic cylinder are performed in this work. The eccentricity e ranges from 0 to 1.2 mm, and the impact velocity ranges from 0.5 to 2 m/s. The effects of the impact velocity and eccentricity are studied in detail. The results show that increasing the eccentricity e reduces the maximum spreading factor and exacerbates the asymmetry of droplets in the azimuthal direction. When the droplets impact on the small cylindrical surface, two collision modes are observed: an asymmetric stretching regime and a stretched rebound regime. The formulation (Wecr/D∗=230ε+31) is employed as a criterion to distinguish between the two modes. With increasing eccentricity e, an asymmetrical flow of droplets from the non-impact side to the impact side occurs, accompanied by a transition in the dynamic behavior of the droplets from stretching to bouncing. The asymmetrical stretching and stretched rebound can effectively decrease the contact time between the droplet and the cylindrical surface, resulting in a reduction of up to 32% during eccentric impact.

Importance of the effects of spreading factor interference in collisions LoRa radio interface

The Internet of Things (IoT) is a concept in which everyday objects and devices are equipped with Internet connectivity, allowing them to exchange information. In recent years, interest in this concept has increased which has led to the creation of numerous applications and interested customers. The Long Range (LoRa) technology is part of the technologies that support IoT equipment LoRa technology has increased a IoT of popularity due to its ability to work with a low power consumption, a very important factor when referring to IoT devices, as they are very dependent on your battery. In this work, we study the importance of interference in the Spreading Factor (SF) and collision effects for the LoRaWAN Radio Interface. To achieve the intended objectives, we will start by making an in-depth study of some types of Wireless Sensor Networks (WSN) networks. Next, we will carry out practical tests in outdoor environment. More than a thousand practical measurements were made between a gateway and a LoRa device, which will be explained throughout this paper. These measurements are used to compare the real values with the theoretical ones for importance of the SF interference in collisions.

CSMA Protocol performance with dynamic spreading factor in LPWAN networks with LoRa RF transceivers

Long Range (LoRa) is a spread spectrum modulation technology designed for Low-Power Wide-Area Networking (LPWAN) with a growing application in Internet of Things (IoT), which plays an important role in the global digitalization process. The expansion of LORAWAN networks demands careful circuit, system and network design to improve energy efficiency and quality of service. To support this task, advanced CAD tools can offer reliable network simulations results, where the effects of building blocks can be found. LoRaSim, one of the most popular simulators available for LoRa and LoraWan, had solely implemented an ALOHA access model protocol, which is not the most common protocol in real life scenarios. To mitigate this limitation and taking advantage of the open-source nature of both LoRaWAN and LoRaSim, it is proposed a Carrier Sense Multiple Access/ Collision Avoidance (CSMA/CA) protocol extension, as well as active adaptive parameters selection to cope with collisions. To demonstrate the effectiveness of these additions to the base solution, a comparison between both on different scenarios and their simulated performance key indicators will be presented.

Interfacial Behavior and Dechlorination Reaction of Water Droplet Impact on a Heated Extracted Titanium Tailing Surface.

The interfacial behaviors of the droplet impact on a heated extracted titanium tailing surface are studied experimentally. The effects of surface temperatures and Weber numbers on the droplet spreading characteristics are examined. The factors affecting the mass fraction and dechlorination ratio of extracted titanium tailings under the action of interfacial behavior have been researched by thermogravimetric analysis. The compositions and microstructures of extracted titanium tailings are characterized using X-ray fluorescence spectroscopy and scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS). The interfacial behaviors on the extracted titanium tailing surface are classified into four regimes, i.e., boiling-induced break-up, advancing recoiling, splash with a continuous liquid film, and splash with a broken film. The maximum spreading factors increase with the surface temperature and the Weber number. It is found that the surface temperature has a dominant influence on the spreading factors and interfacial effect, further affecting its chlorination reaction. SEM-EDS analysis revealed that the extracted titanium tailing particles are irregular shaped. There are some fine pores on the surface after the reaction. The main concentrations are Si, Al, and Ca oxides with a certain amount of C elements. The findings of this research provide a new pathway to utilize the extracted titanium tailings comprehensively.

Jamming of LoRa PHY and Countermeasure

LoRaWAN forms a one-hop star topology where LoRa nodes send data via one-hop uplink transmission to a LoRa gateway. If the LoRa gateway can be jammed by attackers, it may not be able to receive any data from any nodes in the network. Our empirical study shows that although the LoRa physical layer (PHY) is robust and resilient by design, it is still vulnerable to synchronized jamming chirps. Potential protection solutions (e.g., collision recovery, parallel decoding) may fail to extract LoRa packets if an attacker transmits synchronized jamming chirps at higher power. To protect the LoRa PHY from such attacks, we propose a new protection method that can separate LoRa chirps from jamming chirps by leveraging their difference in power domain. We note that the new protection solution is orthogonal to existing solutions that leverage the chirp misalignment in the time domain or the frequency disparity in the frequency domain. We conduct experiments with COTS LoRa nodes and software-defined radios with varied experiment settings such as different spreading factors, bandwidths, and code rates. Results show that synchronized jamming chirps at high power can jam all previous solutions, whereas our protection solution can effectively protect LoRa gateways from the jamming attacks.

Adaptive Parameters for LoRa-Based Networks Physical-Layer.

Sub-GHz communication provides long-range coverage with low power consumption and reduced deployment cost. LoRa (Long-Range) has emerged, among existing LPWAN (Low Power Wide Area Networks) technologies, as a promising physical layer alternative to provide ubiquitous connectivity to outdoor IoT devices. LoRa modulation technology supports adapting transmissions based on parameters such as carrier frequency, channel bandwidth, spreading factor, and code rate. In this paper, we propose SlidingChange, a novel cognitive mechanism to support the dynamic analysis and adjustment of LoRa network performance parameters. The proposed mechanism uses a sliding window to smooth out short-term variations and reduce unnecessary network re-configurations. To validate our proposal, we conducted an experimental study to evaluate the performance concerning the Signal-to-Noise Ratio (SNR) parameter of our SlidingChange against InstantChange, an intuitive mechanism that considers immediate performance measurements (parameters) for re-configuring the network. The SlidingChange is compared with LR-ADR too, a state-of-the-art-related technique based on simple linear regression. The experimental results obtained from a testbed scenario demonstrated that the InstanChange mechanism improved the SNR by 4.6%. When using the SlidingChange mechanism, the SNR was around 37%, while the network reconfiguration rate was reduced by approximately 16%.

Numerical investigation of impacting heat transfer of binary droplets on superhydrophobic substrates

The droplet impacting on a substrate is widely encountered in daily life and industrial applications. In this paper, the impact dynamics and heat transfer are numerically simulated for both single droplet and binary droplets on a hot superhydrophobic substrate. The dimensionless numerical model is built up through the transient 2D axisymmetric model with a volume of fluid (VOF) model. The effect of the Weber number, Reynolds number, size ratio, contact angle on the spreading dynamics and heat transfer is investigated in details. It is found that the spreading factor and contact time increase with the increasing Weber number. Besides, the maximum spreading factor and contact time of binary droplets are larger than those of a single droplet under the same Reynolds and Weber numbers. The transient heat transfer rate of a single droplet is larger than that of binary droplets impingement due to larger spreading surface area-to-volume. The total input heat of a single droplet is generally larger than that of binary droplets except at large Weber numbers, and the equal-sized binary droplets have the larger total input heat than un-equal-sized binary droplets. For binary droplets impact on a hot superhydrophobic surface, the hot substrate can promote the spreading and retard the receding due to thermo-capillary effect, and thus will enhance heat transfer between the droplet and the hot substrate. These findings may be helpful in gaining insights into the dynamics and heat transfer of binary droplets impact on the hot substrate.

An Enhanced Probabilistic-Shaped SCMA NOMA for Wireless Networks

The future digital evolution poses challenges that need to be spectral and energy-efficient, as well as highly reliable and resilient. The non-orthogonal multiple access (NOMA) accomplishes massive connectivity, spectral efficiency, effective bandwidth utilization, and low latency. The proposed work involves the code domain NOMA scheme called Sparse Code Multiple Access (SCMA) which provides shaping gain through multi-dimensional constellation and the best performance in terms of bit error rate (BER). It achieves overloading of users through the non-orthogonal allocation of resources which enhances the spectral efficiency and serves more users. The shaping gain can be further improved by reducing the BER and enhancing the capacity of the channel through constellation shaping. This work employs a probabilistic-shaped (PS) constellation where each symbol is transmitted with different probabilities which achieves a reduction of average symbol power and forward error correction (FEC) through channel coding using polar codes which aid in energy efficiency. The output is two-dimensionally spread over Orthogonal Frequency Code Division Multiplexing (OFCDM) subcarriers to achieve a flexible transmission rate through a variable spreading factor. Computer simulations showed better BER performance under AWGN and Rayleigh channels with remarkable gain in SNR which paves the way for future applications in Fifth Generation (5G) beyond networks.

Approximate BER Performance of LoRa Modulation With Heavy Multipath Interference

This letter proposes a method to approximate LoRa performance over heavy multipath channels. Firstly, the multipath time delay and LoRa signal duration are combined to analyze the path overlapping interference quantitatively. Then simple BER approximations in coherent and non-coherent detections are derived with Gaussian Q-function and algebraic formulas. With the heavy multipath channel model in 3GPP to mimic the realistic indoor industrial scenarios, the approximate expressions are verified by Monte Carlo simulations. Moreover, performance comparison results show that a high spreading factor leads to severe multipath overlap and may result in BER reduction.

Design of a networking bolted joints monitoring method based on PZT

As the failure of bolt connections by corrosion can result in major disasters and cause casualties and property damage, monitoring bolted joints is of great importance. However, current researches on lamb wave based bolted joint monitoring mainly focused on single-input-single-output (SISO) systems, which require a long diagnosis time and numerous transducers. To reduce the number of transducers on monitoring, the multiple-input systems, i.e. multiple-input-multiple-output (MIMO) system and multiple-input-single-output (MISO) system, can be adopted. However, both multiple-input systems are prone to failure due to the interference among the excited waves generated from multiple simultaneously-operated actuators. To remove such interference, an orthogonal variable spreading factor (OVSF) code based bolted joint monitoring method is proposed here. Firstly, instead of using the same detection signal, multiple detection signals generated using OVSF codes are emitted from different input ports. Then, for each considered input port, the corrosion information carrying waveforms are recovered from the acquired signals via timing acquisition and demodulation and utilized to reveal the status of the bolted joints via wavelet packet based analysis. To validate the proposed method, experiments with a MIMO system (i.e. three-input-two-output system) and a MISO system (i.e. six-input-single-output system) were conducted to simultaneously reveal six given bolted joint corrosion status in this research. Since the proposed method effectively suppresses the interference, the MIMO/MISO bolted joint monitoring system can present a performance similar to that of the SISO monitoring system but require fewer transducers.