scholarly journals Designing LoRaWAN Network For Unbiased Communication Between Nodes and Gateway

2021 ◽  
Author(s):  
poonam Maurya ◽  
Aatmjeet Singh ◽  
Arzad Alam Kherani
Keyword(s):  
System Performance ◽  
Success Probability ◽  
Target System ◽  
Mathematical Framework ◽  
Area Network ◽  
Wide Area Network ◽  
Spreading Factor ◽  
Equal Area ◽  
Simulation Results ◽  
Selection Of

Abstract Proper cell designing is required to achieve a target system performance in Long Range Wide Area Network (LoRaWAN). This paper addresses a suitable selection of network designing parameters problem, such as the dimension of different spreading factors’ annuli (zones or SF boundaries) in a LoRaWAN cell. We propose a mathematical framework for designing the LoRaWAN network. The main objective is to ensure that distributed end devices in the network can have the same success probability, irrespective of the spreading factor usage and their locations, unlike Equal Area Based (EAB) based network. We further enhance the performance of the network based on the proposed dimensions by adopting the k-tolerance algorithm. When the network follows the proposed dimensions, simulation results show an improvement in overall success probability over the traditional EAB scheme. In the later part of the paper, we address urbanization issues that degrade the system performance. In our approach to recoup the degradation in the system performance, we implement k−tolerance algorithm in the network.

scholarly journals K-Means Spreading Factor Allocation for Large-Scale LoRa Networks

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4723 ◽  
Author(s):  
Muhammad Asad Ullah ◽  
Junnaid Iqbal ◽  
Arliones Hoeller ◽  
Richard Souza ◽  
Hirley Alves
Keyword(s):  
Low Power ◽  
Long Range ◽  
Large Scale ◽  
Research Work ◽  
Wide Area ◽  
Area Network ◽  
Wide Area Network ◽  
Spreading Factor ◽  
Worst Case ◽  
Simulation Results

Low-power wide-area networks (LPWANs) are emerging rapidly as a fundamental Internet of Things (IoT) technology because of their low-power consumption, long-range connectivity, and ability to support massive numbers of users. With its high growth rate, Long-Range (LoRa) is becoming the most adopted LPWAN technology. This research work contributes to the problem of LoRa spreading factor (SF) allocation by proposing an algorithm on the basis of K-means clustering. We assess the network performance considering the outage probabilities of a large-scale unconfirmed-mode class-A LoRa Wide Area Network (LoRaWAN) model, without retransmissions. The proposed algorithm allows for different user distribution over SFs, thus rendering SF allocation flexible. Such distribution translates into network parameters that are application dependent. Simulation results consider different network scenarios and realistic parameters to illustrate how the distance from the gateway and the number of nodes in each SF affects transmission reliability. Theoretical and simulation results show that our SF allocation approach improves the network’s average coverage probability up to 5 percentage points when compared to the baseline model. Moreover, our results show a fairer network operation where the performance difference between the best- and worst-case nodes is significantly reduced. This happens because our method seeks to equalize the usage of each SF. We show that the worst-case performance in one deployment scenario can be enhanced by 1 . 53 times.

scholarly journals Resource Allocation to Massive Internet of Things in LoRaWANs

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2645 ◽  
Author(s):  
Arshad Farhad ◽  
Dae-Ho Kim ◽  
Jae-Young Pyun
Keyword(s):  
Collision Probability ◽  
Delivery Ratio ◽  
Area Network ◽  
Loss Ratio ◽  
Wide Area Network ◽  
Spreading Factor ◽  
Recovery Algorithm ◽  
Network Concept ◽  
Simulation Results ◽  
The Impact

A long-range wide area network (LoRaWAN) adapts the ALOHA network concept for channel access, resulting in packet collisions caused by intra- and inter-spreading factor (SF) interference. This leads to a high packet loss ratio. In LoRaWAN, each end device (ED) increments the SF after every two consecutive failed retransmissions, thus forcing the EDs to use a high SF. When numerous EDs switch to the highest SF, the network loses its advantage of orthogonality. Thus, the collision probability of the ED packets increases drastically. In this study, we propose two SF allocation schemes to enhance the packet success ratio by lowering the impact of interference. The first scheme, called the channel-adaptive SF recovery algorithm, increments or decrements the SF based on the retransmission of the ED packets, indicating the channel status in the network. The second approach allocates SF to EDs based on ED sensitivity during the initial deployment. These schemes are validated through extensive simulations by considering the channel interference in both confirmed and unconfirmed modes of LoRaWAN. Through simulation results, we show that the SFs have been adaptively applied to each ED, and the proposed schemes enhance the packet success delivery ratio as compared to the typical SF allocation schemes.

scholarly journals An Adaptive Spreading Factor Selection Scheme for a Single Channel LoRa Modem

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1008 ◽  
Author(s):  
Seungku Kim ◽  
Heonkook Lee ◽  
Sungho Jeon
Keyword(s):  
Low Power ◽  
Long Range ◽  
Single Channel ◽  
Wide Area ◽  
Area Network ◽  
Wide Area Network ◽  
Maximum Throughput ◽  
Spreading Factor ◽  
Selection Scheme ◽  
Data Rates

When the low power wide area network (LPWAN) was developed for the internet of things (IoT), it attracted significant attention. LoRa, which is one of the LPWAN technologies, provides low-power and long-range wireless communication using a frequency band under 1 GHz. A long-range wide area network (LoRaWAN) provides a simple star topology network that is not scalable; it supports multi-data rates by adjusting the spreading factor, code rate, and bandwidth. This paper proposes an adaptive spreading factor selection scheme for corresponding spreading factors (SFs) between a transmitter and receiver. The scheme enables the maximum throughput and minimum network cost, using cheap single channel LoRa modules. It provides iterative SF inspection and an SF selection algorithm that allows each link to communicate at independent data rates. We implemented a multi-hop LoRa network and evaluated the performance of experiments in various network topologies. The adaptive spreading factor selection (ASFS) scheme showed outstanding end-to-end throughput, peaking at three times the performance of standalone modems. We expect the ASFS scheme will be a suitable technology for applications requiring high throughput on a multi-hop network.

scholarly journals Performance Analysis of LPWAN Using LoRa Technology for IoT Application

2018 ◽  
Vol 7 (4.11) ◽  
pp. 212 ◽  
Author(s):  
Amir Muaz Abdul Rahman ◽  
Fadhlan Hafizhelmi Kamaru Kamaru Zaman ◽  
Syahrul Afzal Che Abdullah
Keyword(s):  
Signal To Noise Ratio ◽  
Autonomous Vehicle ◽  
New Product ◽  
Wide Area ◽  
Area Network ◽  
Wide Area Network ◽  
Spreading Factor ◽  
Status Information ◽  
Signal Fading ◽  
Vehicle Status

This paper was dedicated to study the performance of an Internet of Things (IoT) application using LoRa Wide Area Network (LoRaWAN). LoRa is a Low Power Wide Area Network (LPWAN) technology developed for IoT applications specifically. Due to the facts that LoRa is a new product, there are questions about its reliability. Hence, a conclusive experiment has been made. The experiment conducted to get an insight to LoRa received signal strength (RSSI) and packet loss. The analysis also includes a measurement of the application Signal to Noise Ratio (SNR) between the transmitter and receiver. The results of the experiment show that with a higher spreading factor, LoRa end device provides more immunity against multi-path and signal fading. The proposed IoT application based on this LoRa technology is for autonomous vehicle status information transmission and intervehicle communications, specifically deployed in UiTM Autonomous Vehicle 1 (UiTM AV1).  

scholarly journals LoRaWAN Energy Optimization with Security Consideration

2021 ◽  
Author(s):  
Ala Khalifeh ◽  
Khaled Aldahdouh ◽  
Sahel Alouneh
Keyword(s):  
Energy Consumption ◽  
Area Network ◽  
Transmission Power ◽  
Wide Area Network ◽  
Spreading Factor ◽  
Transmission Parameters ◽  
Potential Applications ◽  
Network Operation ◽  
Use Of The Internet ◽  
And Performance

Long Range Wide Area Network (LoRaWAN) is an emerging wireless technology that is expected to be widely deployed and implemented in several applications, especially with the promising widespread use of the Internet of Things (IoT) and its potential applications within the Fifth Generation (5G) communication technology. LoRaWAN consists of a number of nodes that monitors and senses the environment to collect specific data, and then sends the collected data to a remote monitoring device for further processing and decision-making. Energy consumption and security assurance are two vital factors needed to be optimized to ensure an efficient and reliable network operation and performance. To achieve that, each of LoRaWAN nodes can be configured by five transmission parameters, which are the spreading factor, carrier frequency, bandwidth, coding rate and transmission power. Choosing the best values of these parameters leads to enhancing the network deployment. In this paper, we shed the light to the security aspect in LoRaWAN network. Then, we introduced an algorithm that depends on the reinforcement learning approach to enable each node in the network to choose the best values of spreading factor and transmission power such that it leads to a lower energy consumption and higher packets’ delivery rate. The results of the simulation experiments of our proposed technique showed a valuable increase in the packet reception rate at the gateway and a significant decrease in the total consumed energy at the end nodes compared with the most related work in literature

scholarly journals Energy Constrained Optimization for Spreading Factor Allocation in LoRaWAN

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4417 ◽  
Author(s):  
Shusuke Narieda ◽  
Takeo Fujii ◽  
Kenta Umebayashi
Keyword(s):  
Energy Consumption ◽  
Performance Improvement ◽  
Long Range ◽  
Spread Spectrum ◽  
Average Energy ◽  
Area Network ◽  
Wide Area Network ◽  
Spreading Factor ◽  
Average Energy Consumption ◽  
Distributed Genetic Algorithm

This paper discusses a spreading factor allocation for Long Range Wide Area Network (LoRaWAN). Because Long Range (LoRa) is based on chirp spread spectrum that each spreading factor is approximately orthogonal to each other, the performance of LoRaWAN can be enhanced by allocating the spreading factor appropriately to end devices (EDs). Several spreading factor allocation techniques have been reported. Techniques shown in existing studies can improve some characteristics (e.g. throughput or packet reception probability (PRP)); however, there are a few studies that have focused on the energy consumption of the EDs. The LoRa communication offers a low power communication and this enables the improvement of the performance in exchange for the energy consumption. This paper presents a performance improvement technique via spreading factor allocations for LoRaWAN. We define the optimization problem for the spreading factor allocation to maximize the PRP under a constraint for the average energy consumption of all the EDs. It enables for the performance improvement under the constraint of the average energy consumption of all the EDs by solving the problem. This study further develops a method to solve the defined problem based on a distributed genetic algorithm, which is metaheuristics method. Although the techniques shown in the existing studies give the average energy consumption as a result of the performance improvement by the spreading factor allocation, the presented technique can enhance the LoRaWAN performance by allocating the spreading factor to EDs under the constraint for the average energy consumption of all the EDs. Numerical examples validate the effectiveness of the presented technique. The PRP performance of the presented technique is superior to that of the techniques shown in the existing studies despite that the average energy consumption of all the EDs of the presented technique is less than that of the techniques shown in the existing studies.

scholarly journals Performance Analysis of LPWAN Using LoRa Technology for IoT Application

2018 ◽  
Vol 7 (4.11) ◽  
pp. 252
Author(s):  
Amir Muaz Abdul Rahman ◽  
Fadhlan Hafizhelmi Kamaru Zaman ◽  
Syahrul Afzal Che Abdullah
Keyword(s):  
Signal To Noise Ratio ◽  
Autonomous Vehicle ◽  
New Product ◽  
Wide Area ◽  
Area Network ◽  
Wide Area Network ◽  
Spreading Factor ◽  
Status Information ◽  
Signal Fading ◽  
Vehicle Status

This paper was dedicated to study the performance of an Internet of Things (IoT) application using LoRa Wide Area Network (LoRaWAN). LoRa is a Low Power Wide Area Network (LPWAN) technology developed for IoT applications specifically. Due to the facts that LoRa is a new product, there are questions about its reliability. Hence, a conclusive experiment has been made. The experiment conducted to get an insight to LoRa received signal strength (RSSI) and packet loss. The analysis also includes a measurement of the application Signal to Noise Ratio (SNR) between the transmitter and receiver. The results of the experiment show that with a higher spreading factor, LoRa end device provides more immunity against multi-path and signal fading. The proposed IoT application based on this LoRa technology is for autonomous vehicle status information transmission and intervehicle communications, specifically deployed in UiTM Autonomous Vehicle 1 (UiTM AV1).   

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