Multihop Uplink Communication Approach Based on Layer Clustering in LoRa Networks for Emerging IoT Applications

Bomgni, Alain Bertrand; Ali, Hafiz Munsub; Shuaib, Mohammed; Mtopi Chebu, Yann

doi:https://doi.org/10.1155/2023/5828671

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Federated Intelligence in Edge and IoT Networks

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Research Article | Open Access

Volume 2023 | Article ID 5828671 | https://doi.org/10.1155/2023/5828671

Multihop Uplink Communication Approach Based on Layer Clustering in LoRa Networks for Emerging IoT Applications

Alain Bertrand Bomgni,^1,2Hafiz Munsub Ali,³Mohammed Shuaib,^4,5and Yann Mtopi Chebu²

Academic Editor: Junaid Shuja

Received02 Nov 2022

Revised20 Nov 2022

Accepted25 Apr 2023

Published20 May 2023

Abstract

LoRa technology is widely used in the Internet of things network applications. It enables low-volume data transmission via small wireless devices. The principle of LoRa networks (an LPWAN technology: Low Power Wide Area Network) is to transmit data by air from sensors with a short transmission range, about over ten kilometers. These sensors should not be powered by electricity, and batteries power them. Hospital visits are inevitable, but current advances in communication could reduce the burden on hospitals with remote (from home) treatments using these wireless sensors. Thus, using the LoRaWAN protocol could greatly facilitate patient diagnosis by transmitting data between doctors and patients in real time and with minimal energy consumption. The objective of this work is to set up a multihop IoT network containing a large number of sensors based on LoRa for uplink communication. This work evaluates the energy consumption and the packet delivery rate in the multihop IoT network. The simulation result shows that the proposed approach reduces power consumption by 50% and improves the packet delivery rate by 2% compared to the existing state of the art.

1. Introduction

The Internet of things (IoT) is experiencing considerable scientific expansion in several areas, facilitating the exchange of information between objects, hardware, and software resources [1–5]. Technological advancements in the field of IoT have enabled the development of a wide variety of applications such as precision agriculture (PA), smart city, asset tracking, health care, and many more [6, 7]. Most of these applications require long-range communications with characteristics such as low data rate, low deployment cost, low power consumption, and security management [8–11]. Thus, short range radio technologies are not very suitable for wide coverage applications. A wide range of data acquisition objects already participate in a broad spectrum of applications that significantly consider long-range communication with low power consumption to extend network life without human intervention [11]. Thus, these growing needs have led to the emergence of Low Power Wide Area Networks (LPWANs). LPWAN is a type of wireless communication that is positioned as a connectivity facilitator for IoT applications or for cellular networks. Indeed, cellular solutions have the advantage of offering long connectivity but are energy intensive [11, 12]. LPWAN technology has experienced considerable growth because it has a set of interesting features among which we can mention scalable deployment, high energy efficiency, low management, and operating costs. Indeed, LPWAN technology makes it possible to build networks made up of thousands of objects with a long range (several kilometers) [13]. Figure 1 [11] presents an architecture of LPWAN technology that facilitates the increase in the number of IoT applications. You can see a panoramic view of the functionalities at different layers as an added value for many applications. In the literature, there are several licensed and unlicensed LPWAN technologies, and among them, the most used solutions are LoRaWAN, Sigfox, and NB-IoT [11, 14].

LoRaWAN is an LPWAN-based communication solution that was designed to take advantage of a long-range, low-cost, and low-power hub-and-spoke network [15]. A LoRaWAN network consists of three essential components as follows: the terminals, the gateway, and then a link to the server, as shown in Figure 2. In addition, security (end-to-end encryption in the various communications) is an important element taken into account in LoRaWAN solutions. Such a LoRaWAN network consists of objects equipped with sensors or actuators that use the LoRa physical layer to exchange messages with the [16] gateway.

Remote health care and remote patient monitoring are concepts that have captured the attention of researchers in recent years [7]. Indeed, a smart home equipped with several objects capable of remotely monitoring one or more patients appears to be an interesting solution nowadays. Specifically, such technology can be an alternative for monitoring disabled people, quarantined people, and people with chronic illnesses. IoT provides an environment of objects connected to cloud-based applications and services, with different cooperation mechanisms, appropriate standardization, and advanced sensors with low-cost and low-power microprocessors [17, 18]. According to Figure 3, LoRaWAN is considered as one of the best IoT solutions based on the healthcare monitoring system due to its wide communication range and perfect interoperability between IoT sensors.

In this work, we have explained a multihop communication protocol over a large-scale area with LoRa devices while ensuring a deterministic and intelligent choice of the gateway which must redirect the information submitted by a device to guarantee, for example, a real-time information treatment by the server while minimizing the amount of energy.

This protocol takes place in two phases. A first phase consists of subdividing the networks into different layers and a second phase during which each device chooses the relay gateway for uplink communications.

We proposed a layered multihop clustering method for structuring large-scale networks. For this purpose, we supposed that the communications between two-end devices are reliable. The proposed algorithm tries to minimize the energy consumption spent during the formation of the layers. We obtained an acceptable packet delivery rate, and the number of messages sent is very low. Moreover, the proposed protocol is energy efficient extending by the way of IoT device battery life time, and it has a linear time complexity which is an asset for real-time IoT applications.

The rest of this paper is organized as follows: Section 2 presents a state of the art on some LoRa protocols, a mathematical formulation of the problem is presented in Section 3, our approach is presented in Section 4, the results of the experiments are presented in Section 5, and finally, the conclusion and some future works are presented in Section 6.

2. Review of the Literature

2.1. LoRa and LoRaWAN

Long Range (LoRa) is Semtech’s spread spectrum modulation technique, a proprietary communication standard that enables long range, single-hop communications. With LoRa technology, it is possible to decode transmissions of up to 19.5 dB on a noise floor [21–23]. This technique allows the reception of a multiple number of messages on channels, an orthogonal separation between the signals. This offers an advantage in the management of the flow.

A characteristic of LoRa is that it offers an improvement of criteria such as SF (spreading factor), TP (transmission power), CR (coding rate), and BW (bandwidth) with a relation given by equation (1) [23–25].

This technology is based on the ISM (industrial, scientific, and medical) bands, whose distribution of frequencies and regulations vary according to the region of the world. The two frequencies mainly used are 868 MHz in Europe and 915 MHz in North America. This physical layer relies on an alternative Spread Spectrum Modulation (SSM) called Chirp Spread Spectrum (CSS) which spreads the base signal over a given frequency domain and increases mean bandwidth with the aim of increasing resistance to interference, reducing energy consumption, and integrating an error-correcting code. This type of modulation, widely used for radar applications in the past, is now necessary for low-speed communications. During a transmission, the spreading factor is adjustable and imposes a preliminary search to optimize the value according to various criteria. Indeed, starting from a fixed pass-band, a high SF directly implies an increase in the range and in the transmission delay of a symbol expressed in seconds. The latter can be calculated using Formula (2). On the other hand, still in the case of a high SF, the communicating system sees its bit rate decreasing but has better reception sensitivity. Formula (1) presents the relationship between the final flow rate and the spreading factor [26].

In LoRa, there are several types of equipment among which we can mention the following: terminals, gateways, servers, application servers, and Join Server; a layout of a LoRa architecture is given by Figure 4.

LoRaWAN is a robust solution competing with other LPWANs by taking up the strengths of LoRa technology while having a strong influence on battery life, network load capacity, quality of service, and security. The LoRaWAN network is organized according to a star of stars topology. The communication proposed in this network is bidirectional while clearly favoring uplink transmissions towards the concentrators [15]. This organization, illustrated in Figure 4, is in fact composed of a multitude of concentrators which relay the information received from the nodes to a central server using a GSM or Ethernet protocol most of the time. The central server makes it possible to eliminate duplicates received by the various concentrators and manages the flow rates of the nodes in order to optimize the capacity of the network and to extend the autonomy of the wireless devices.

2.2. Multihop Communications in LoRa Networks

In [22], the authors propose an algorithm which addresses three main criteria which are load balance in the network, reduction of the number of hops between the root, and a terminal and connectivity problem, using a faster throughput (higher SF) for connections for insertion of each node away from the root with the Topdown Breadth First-Search (TBFS) algorithm. This protocol uses long range communications for subtree formations, which requires a higher spreading factor and therefore additional energy consumption.

In [24], the author proposes new multihop clustering algorithm LoRaWAN networks. This approach is divided into two steps. The first one allows to form the layers, and the second one allows to choose the gateway for the communications to the root. For the layer formation, the protocol modifies the initial structure by adding elements of layer identifications by the gateways. The process is managed by the root which sends a broadcast with a hop count of 0 for the identification of the first layer. The answers of the elements of this layer allow him to make another diffusion for the identification of the following layer by passing by layer 0. Layer 1 sends an answer, and the process proceeds thus until obtaining all the layers. The selection is made from the RSSI of each gateway except for this one at a layer lower than a node. It has been compared to the method proposed in [28]. It also proposes an approach of multihop communication in LoRa and has LoRaWAN. This method thus allows a good communication between terminals over a long distance. However, the layer formation mechanism is very tedious when the number of layers is very high and would consume a lot of time and energy.

In [29], a protocol that maintains connectivity and coverage with the network using multihop communications is proposed. Evaluations are made on parameters such as the spreading factor and the packet reception rate. This protocol shows how in a smart city, the use of LoRa technology with multihop communications can save energy, ensure network coverage, and good connectivity. However, the use of the spread spectrum factor is still very high. This increases the energy consumption of the nodes.

In [30], it is proposed to use a simple relay device to increase the LoRaWAN coverage area for rural areas. The authors suggested deploying relay nodes by knowing the locations that are not covered by the gateway. The authors proposed a simple message forwarder and a synchronization mechanism. They showed that the energy consumption decreases with the addition of the relay node to deliver the packets. This method saves energy by minimizing retransmissions and increases the network connectivity rate. However, this protocol is only valid for a maximum of 2 hops. This does not allow for scalability.

In [31], the authors propose a transmission protocol (concurrent transmission) which proceeds by flooding the network for the design of multihop communications. The transmission of packets is done by transmitting identical packets in a synchronous manner, which increases the efficiency of the network by solving the problem of packet collisions. Although this protocol improves the efficiency of the multihop network and is robust against collisions, it results in a huge energy consumption due to the numerous identical messages broadcasted through the network.

3. Problem Formulation

We consider the set of gateways in the network, DV = the set of LoRa terminals in the network that need to be dispersed in layers, and the set of formed layers in the network. We seek to improve the operation of large-scale LPWANs by considering the multiobjective problem (energy-efficient formation of layers and intelligent selection of the best gateway). The selection of the best gateway is given byoù.(1) is the set of gateways determined as follows:(2)f is the objective function of each terminal in relation to a gateway.

This problem is formulated subject to the constraints as follows:(1)Let be a binary value which indicates the admission of the device to the gateway .(2).(3), .

4. Multihop Uplink Communication Approach

In this section, we present an efficient approach to multiobjective routing in a LoRa network based on layer clustering. This approach is divided into two steps. The first one is to form layers, and the second one is to select the best gateway for routing data to the root.

4.1. Assumptions

(1)The nodes are randomly deployed on a square surface(2)The transmission radio is the same for each equipment(3)The gateways are deployed in a deterministic way to ensure the coverage of the deployment area

4.2. Layer Formation

Each gateway has the following parameters for layer formation:(1)id represents the identifier of the gateway(2) is the layer number to which the gateway belongs(3) contains the id of its gateway to reach the root gateway

We define three types of messages for layer formation. A DIL (Discover Information Layer) and DIA (Discover Information Acceptance) which contain the fields for the layer number, is the number of hops from the root gateway, is the identifier of the node transmitting a DIA or DIL message, and finally, a contains the identifier of the relaying gateway towards the root. A DIN (Direct Information Notification) message contains the fields for the identifier of the transmitting gateway, contains the identifier of the relay gateway to the root, and contains the number of layers of the formed network.

The root gateway initiates the broadcast of a DIA message with the LID field set to 0 to indicate the start of the layering process. As soon as a DIS message is received, each gateway initiates a DIA message to indicate that the message has been received and initiates a DIS message again, this time with the LID number incremented by 1 and the hop number set to 1. It then sets a timer t and waits until the end of this time. If it has received another DIL message, it generates a DIL message and transmits it to its relay gateway.

Otherwise, it generates a DIL message and forwards it to its relay gateway and thus considers itself as the leaf node of the layered tree. The process repeats itself until layer n − 1 receives DILs from layer n, transmits DILs to layer n − 2, and so on until the DILs are distributed to the root. This will allow the root gateway to know the number of layers formed in the network. Algorithm 1 is executed by all the gateways for the formation of the network layers.

	layer_id = ;
	if id = = 0 then
	CREATE_DIS_MESSAGE (1, 0, 0, 0);
	BROADCAST (DIS);
	end if
	if DIS message then
	if DIS. ¡ then
	=
	CREATE_DIA_MESSAGE ( + 1, + 1, + 1, ID);
	UNICAST (DIA);
	CREATE_DIS_MESSAGE (, ID);
	BROADCAST (DIS);
	EXPIRER (timer);
	;
	if EXPIRER (timer) then
	if Not DIA reception then
	CREATE_DIN_MESSAGE (id, layer_id, );
	UNICAST (DIN)
	end if
	end if
	end if
	end if
	if DIN message then
	CREATE_DIN_MESSAGE (ID, gtwlayer_id, layer_id);
	UNICAST (DIN);
	end if

An illustration of this approach is presented in Figure 5. The root gateway in the center of the figure (purple color) illustrates the formation of the layers. The other gateways in green, red, and blue colors are Layer 1, Layer 2, and Layer 3 gateways, respectively.

4.3. Gateway Selection

This step consists of intelligently choosing a gateway in its layer to be able to route information to the root gateway. Once the layer formation is complete, each gateway will issue a broadcast message to tell each device which gateways in its layer, the upper and/or perhaps lower layer, where it can access. Each node will calculate its objective function to determine which gateway should be chosen to minimize energy consumption costs. Algorithm 2 is executed by all the end devices for the selected gateway.where RSSI (Received Signal Strength Indication) is the received signal strength, SF (spreading factor) and is the layer number of the gateway, and .

	node in network;
	receive Broadcast;
	Compute for Gateway with formula ;
	Select max and send it unicast_message;

For each gateway message received, the device evaluates the objective function f. It will therefore choose among the gateways in its layer, the gateway with the largest value of f.

5. Results

The simulation is conducted on an i5 series processor with 4 GB of RAM using LoRaSim. LoRaSim is a discrete-event simulator based on SimPy for simulating collisions in LoRa networks and to analyse scalability. Their simulator and the model energy are described in [32].

The simulation parameters are listed in Figure 6.

Figure 7 shows a comparison of the energy consumed by the RPL, Farooq, and LoRaWan protocols. We can observe that our protocol outperforms those of the others by consuming 50%, 70%, and 80% less than the Farooq protocols [24, 28] and LoRaWan.

We also bring an improvement in the packet delivery rate of 2% compared to [24] in Figure 8.

Figure 9 shows the number of messages sent for the layer formation process in the network. We see that compared to the Farooq protocol, the number of messages sent remains constant regardless of the number of layers formed in the network. This proves that the root gateway sends the same number of messages for the formation of the layers whatever the size of the network. So, it also proves that this protocol is suitable for large-scale networks.

Figure 10 shows the number of messages sent for the layer formation process in the network. We see that compared to the Farooq protocol, the number of messages sent remains constant regardless of the number of layers formed in the network. This proves that the intermediate gateways send the same number of messages for the formation of the layers whatever the size of the network. Therefore, the scalability of the network has no impact on the messages sent by the intermediate gateways.

The protocol in the literature review sends too many messages in the case of the root gateway as well as in the case of the intermediate gateways, which indicates without a doubt that the energy consumption of this protocol is extremely high for the formation of layers in the network.

6. Conclusion

In this article, we have proposed a low power system with LoRa technology. This system can be used to monitor the physiological parameters of a patient to determine his medical situation, in order to anticipate the aggravation of pathologies for patients and to reduce the time of hospitalization and cost, especially with the spread of the Covid pandemic. Our results showed very good efficiency in terms of system life, with autonomy gains multiplied by 2. Work is underway to improve the study surface.

Data Availability

No underlying data was collected or produced in this study.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent was obtained from all individual participants included in the study.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

A. B. Bomgni, M. L. F. Sindjoung, D. K. Tchibonsou, M. Velempini, and J. F. Myoupo, “Neseprin: a new scheme for energy-efficient permutation routing in iot networks,” Computer Networks, vol. 214, Article ID 109162, 2022.
View at: Publisher Site | Google Scholar
R. Zi, J. Liu, L. Gu, and X. Ge, “Enabling security and high energy efficiency in the internet of things with massive mimo hybrid precoding,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8615–8625, 2019.
View at: Publisher Site | Google Scholar
X. Ge, Y. Sun, H. Gharavi, and J. Thompson, “Joint optimization of computation and communication power in multi-user massive mimo systems,” IEEE Transactions on Wireless Communications, vol. 17, no. 6, pp. 4051–4063, 06 2018.
View at: Publisher Site | Google Scholar
X. Ge, B. Yang, J. Ye, G. Mao, C.-X. Wang, and T. Han, “Spatial spectrum and energy efficiency of random cellular networks,” IEEE Transactions on Communications, vol. 63, no. 3, pp. 1019–1030, March 2015.
View at: Publisher Site | Google Scholar
J. Yang, X. Ge, J. Thompson, and H. G. Gharavi, “Power-consumption outage in beyond fifth generation mobile communication systems,” IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 897–910, Feb 2021.
View at: Publisher Site | Google Scholar
V. Hassija, V. Chamola, V. Saxena, D. Jain, P. Goyal, and B. Sikdar, “A survey on iot security: application areas, security threats, and solution architectures,” IEEE Access, vol. 7, pp. 82721–82743, 2019.
View at: Publisher Site | Google Scholar
H. M. Ali, J. Liu, S. A. C. Bukhari, and H. T. Rauf, “Planning a secure and reliable iot-enabled fog-assisted computing infrastructure for healthcare,” Cluster Computing, vol. 25, pp. 2143–2161, 2022.
View at: Google Scholar
Hafiz Munsub Ali, W. Ejaz, D. C. Lee, and I. Khater, “Optimising the power using firework-based evolutionary algorithms for emerging iot applications,” IET Nteworks, vol. 15, 2019.
View at: Google Scholar
A. Bomgni, B. Garrik, M. Jagho, M. A. Hafiz, G. Z. David, and G. Z. Etienne, “Espina: efficient and secured protocol for emerging iot network applications,” Cluster Computing, vol. 24, 2022.
View at: Google Scholar
Y. Brice Chebu Mtopi, A. Bertrand Bomgni, H. Munsub Ali, D. R. G. Zanfack, W. Ejaz, and E. Zohim, “Multihop optimal time complexity clustering for emerging iot applications,” Cluster Computing, vol. 26, 2022.
View at: Google Scholar
R. K. Singh, P. P. Puluckul, R. Berkvens, and M. Weyn, “Energy consumption analysis of lpwan technologies and lifetime estimation for iot application,” Sensors, vol. 20, no. 17, p. 4794, 2020.
View at: Publisher Site | Google Scholar
W. Wang and G. Shen, “Energy efficiency of heterogeneous cellular network,” in Proceedings of the 2010 IEEE 72nd Vehicular Technology Conference - Fall, Ottawa, Canada, September 2010.
View at: Google Scholar
K. Mekki, E. Bajic, F. Chaxel, and F. Meyer, “A comparative study of lpwan technologies for large-scale iot deployment,” ICT express, vol. 5, no. 1, pp. 1–7, 2019.
View at: Publisher Site | Google Scholar
W. Ayoub, A. E. Samhat, F. Nouvel, M. Mroue, and J.-C. Prévotet, “Internet of mobile things: overview of lorawan, dash7, and nb-iot in lpwans standards and supported mobility,” IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1561–1581, 2019.
View at: Publisher Site | Google Scholar
LoRaWAN, “Lora alliance,” 2022, https://lora-alliance.org/about-lorawan/.
View at: Google Scholar
LoRaWAN Specification, “Lora alliance,” 2022, https://lora-alliance.org/resource-hub/lorawanr-specification-v11.
View at: Google Scholar
N. Misran, M. S. Islam, G. K. Beng, N. Amin, and M. T. Islam, “Iot based health monitoring system with lora communication technology,” in Proceedings of the 2019 International Conference on Electrical Engineering and Informatics (ICEEI), pp. 514–517, Bandung, Indonesia, July 2019.
View at: Google Scholar
F. A. Muhammad, N. K. Abdul, J. Shuja, A. K. Iftikhar, G. K. Fiaz, and R. K. Atta, “A lightweight and compromise-resilient authentication scheme for iots,” Transactions on emerging telecommunications technologies, vol. 33, 2019.
View at: Google Scholar
A. Lavric and V. Popa, “Performance evaluation of lorawan communication scalability in large-scale wireless sensor networks,” Wireless Communications and Mobile Computing, vol. 2018, Article ID 6730719, 9 pages, 2018.
View at: Publisher Site | Google Scholar
D. Ismail, M. Rahman, and A. Saifullah, “Low-power wide-area networks: opportunities, challenges, and directions,” in Proceedings of the Workshop Program of the 19th International Conference on Distributed Computing and Networking, Varnasi, India, January 2018.
View at: Google Scholar
M. C. Bor, R. Utz, T. Voigt, and J. M. Alonso, “Do lora low-power wide-area networks scale?” in Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, New York City, NY, USA, November 2016.
View at: Google Scholar
G. Zhu, C.-H. Liao, T. Sakdejayont, I.-W. Lai, Y. Narusue, and H. Morikawa, “Improving the capacity of a mesh lora network by spreading-factor-based network clustering,” IEEE Access, vol. 7, pp. 21584–21596, 2019.
View at: Publisher Site | Google Scholar
R. S. Sinha, Y. Wei, and S.-H. Hwang, “A survey on lpwa technology: lora and nb-iot,” Ict Express, vol. 3, no. 1, pp. 14–21, 2017.
View at: Publisher Site | Google Scholar
M. O. Farooq, “Clustering-based layering approach for uplink multi-hop communication in lora networks,” IEEE Networking Letters, vol. 2, no. 3, pp. 132–135, 2020.
View at: Publisher Site | Google Scholar
M. Usmonov and F. Gregoretti, “Design and implementation of a lora based wireless control for drip irrigation systems,” in Proceedings of the 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE), pp. 248–253, Shanghai, China, December 2017.
View at: Google Scholar
X. Wang, M. Fei, and X. Li, “Performance of chirp spread spectrum in wireless communication systems,” in Proceedings of the 2008 11th IEEE Singapore International Conference on Communication Systems, pp. 466–469, Guangzhou, China, November 2008.
View at: Google Scholar
LoRaWAN, Oct 2020, https://lora-developers.semtech.com/library/tech-papers-and-guides/lora-and-lorawan/.
B. Sartori, S. Thielemans, M. Bezunartea, B. An, and K. Steenhaut, “Enabling rpl multihop communications based on lora,” in Proceedings of the 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), October 2017.
View at: Google Scholar
M. S. Aslam, A. Khan, A. Atif et al., “Exploring multi-hop lora for green smart cities,” IEEE Network, vol. 34, no. 2, pp. 225–231, 2020.
View at: Publisher Site | Google Scholar
Diop Mamour and C. Pham, “Increased flexibility in long-range iot deployments with transparent and light-weight 2-hop lora approach,” Wireless Days (WD), vol. 6, 2019.
View at: Google Scholar
C.-H. Liao, G. Zhu, D. Kuwabara, M. Suzuki, and H. Morikawa, “Multi-hop lora networks enabled by concurrent transmission,” IEEE Access, vol. 5, pp. 21430–21446, 2017.
View at: Publisher Site | Google Scholar
M. Bor, R. Utz, T. Voigt, and Juan Alonso, “Do lora low-power wide-area networks scale?” in Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 59–67, New York city, NY, USA, November 2016.
View at: Google Scholar

Copyright

Copyright © 2023 Alain Bertrand Bomgni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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