Relaying node positioning method based on sir / SNR maximization in LORA networks

Abstract

This invention introduces a placement mechanism for a relay (2) device in a LoRa network, aiming to optimize network throughput, average success probability, and coverage probability. The relay (2) device is strategically positioned at a suitable distance from the gateway to establish a direct communication link. This placement aims to optimize the signal-to-noise (SNR) and signal-to-interference (SIR) probabilities. The goal is to position the relay (2) in a way that allows the forwarded signals to exceed the SNR receiver sensitivity threshold, as well as the thresholds for co-SF and cross-SF interference. In contrast to previous approaches that utilize deployed nodes as relay (2) nodes by either selecting the optimal node or determining the optimal SF region for relaying (2) purposes, this method employs an external relay (2) device to be strategically positioned at a precise distance, while ensuring the existing nodes retain their original roles unaffected.

Description

[0001] RELAYING NODE POSITIONING METHOD BASED ON SIR / SNR MAXIMIZATION IN LORA NETWORKS

[0002] Technical Field:

[0003] This invention relates to a relaying node positioning method based on signal-to- interference (SIR) / signal-to-noise (SNR) maximization developed for finding the best positioning / placement of relaying nodes in LoRa networks to increase the coverage area, and the communication performance while minimizing the number of deployed relays.

[0004] State of The Art:

[0005] The LoRa network in present technology has several challenges, including limited communication range in urban and suburban areas, packet collision resulting from cross- SF and co-SF interference, packet loss in out-of-coverage nodes, and low data rate due to restricted duty cycle and initial communication stage requirements. The aforementioned limitations have led to the utilization of relay nodes, hence rendering the selection of relay distribution an unresolved and indeterminate issue.

[0006] Nowadays, diverse algorithms have been utilized to select multiple nodes in an effort to improve the communication's reliability. In [1], the authors explore relay positioning optimization in order to maximize coverage probability. In particular, they devise a non- convex continuous objective function to determine the optimal relay position in various zones, taking into account intra, inter, and intra-inter interference probabilities between various spreading factors (SFs). The work in [2], a novel method for determining the optimal position of an unmanned aerial vehicle (UAV) relay to maximize link quality is proposed. In this method, this is achieved by utilizing channel state sampling at multiple locations and estimating the remaining flight path using a three-dimensional matrix completion technique based on compressed sensing methods. The authors of [3] utilize the received signal strength indicator (RSSI) and angle of arrival (AoA) to guide a relaying UAV to an optimal position, thereby centralizing a network of sensors without prior knowledge of ground EDs. The placement of relaying nodes within a sensor network is addressed in [4] by arbitrarily deploying them. When direct communication between sensors is not possible, these relay nodes facilitate data transmission. The selection of relaying nodes is carried out using a framework of deep neural networks. The work in [5], discusses the deployment and optimization of relays in self-organized networks. The authors propose relay assignment aiding capacity maximization algorithms. Similarly, [6] presents an optimal relaying node scheme that employs numerically-solved optimization problems to balance the signal-to-noise ratio (SNR) of each step using a linear network model. In addition, the optimal placement of relaying nodes is addressed in [7], taking into account a limited set of predefined candidate positions. This is accomplished by utilizing the communication link's signal-to-interference-plus-noise ratio (SINR) and outage probability based on SINR. The authors of [8], consider an iterative algorithm employing block coordinate descent and successive convex approximation techniques is used to optimize UAV-aided relaying positions. In [9], a derivation of the approximate outage probability in the high SNR regime along with a formulation of an optimal relay placement problem to minimize the approximate success probability. In a similar vein, the authors in

[0010] proposes an algorithm that maximizes the throughput of communication nodes based on a derived optimization problem for the joint placement and scheduling of a relay node.

[0007] In addition to the above papers, patent application KR102396399B1 proposes a LoRa relay node selection based on the joint success probability of SNR and SIR. Patent application CN108156613 A, proposes a relay UAV node deployment in a multi-hop communication system by deriving a mathematical optimization problem under the KKT optimization conditions to obtain a closed-form solution for the UAV relay position. The inventors of patent application CN112235824A present a two-hop network outage probability and system throughput analysis employing an end-to-end SNR under Rician fading channel.

[0008] Although proposing a relay placement mechanism based on identifying the optimal node in a multi-hop LoRa network overcomes some LoRa limitations, it contradicts the LoRa Alliance's vision by overburdening the roles of a low-complexity end device. The selection of an existing node as a relay or the designation of an SF region as the optimal region for a relay node restricts the search space when it comes to maximizing system performance, thereby degrading the performance of the relay node. In addition, it is difficult to implement relay node placement based on routing branch principle because signals broadcasts in a real wireless environment that differs from the networking environment.

[0009] As a result, a new method is needed that can overcome the above-mentioned disadvantages.

[0010] References:

[0011] [1] T. H. Nguyen, W.-S. Jung, L. T. Tu, T. V. Chien, D. Yoo, and S. Ro, "Performance analysis and optimization of the coverage probability in dual hop LoRa networks with different fading channels," IEEE Access, vol. 8, pp. 107087-107102, 2020.

[0012] [2] L. Kong, L. Ye, F. Wu, M. Tao, G. Chen, and A. V. Vasilakos, “Autonomous relay for millimeter-wave wireless communications,” IEEE J. Sei. Areas Commun., vol. 35, no. 9, pp. 3949-3963, Sep. 2017.

[0013] [3] A. Chamseddine, O. Akhrif, G. Charland-Arcand, F. Gagnon, and D. Couillard, “Communication relay for multi ground units with unmanned aerial vehicle using only signal strength and angle of arrival,” IEEE Trans. Control Syst. Technol., vol. 25, no. 1, pp. 239-286,

[0014] Apr. 2017.

[0015] [4] T.-V. Nguyen, T.-N. Tran, K. Shim, T.-T. Huynh-The, and B. An, "A deep-neural- network-based relay selection scheme in wireless-powered cognitive loT networks," IEEE Internet of Things Journal, vol. 8, no. 9, pp. 7423-7436, 2020.

[0016] [5] X. Zhong, Y. Guo, N. Li, and Y. Chen, "Joint optimization of relay deployment, channel allocation, and relay assignment for UAVs-aided D2D networks," IEEE / ACM Transactions on Networking, vol. 28, no. 2, pp. 804-817, 2020.

[0017] [6] V. N. Q. Bao, T. T. Thanh, T. D. Nguyen, and T. D. Vu, "Spectrum sharing-based multi-hop decode-and-forward relay networks under interference constraints: Performance analysis and relay position optimization," Journal of Communications and Networks, vol. 15, no. 3, pp. 266-275, 2013.

[0018] [7] M. Bagaa, A. Chelli, D. Djenouri, T. Taleb, I. Balasingham, and K. Kansanen, "Optimal placement of relay nodes over limited positions in wireless sensor networks," IEEE Transactions on Wireless Communications, vol. 16, no. 4, pp. 2205-2219, 2017.

[0019] [8] Q. Chen, "Joint position and resource optimization for multi-UAV-aided relaying systems," IEEE Access, vol. 8, pp. 10403-10415, 2020.

[0020] [9] Han, L., Huang, C., Shao, S., & Tang, Y. (2013). Relay placement for amplify-and- forward relay channels with correlated shadowing. IEEE Wireless Communications Letters, 2(2), 171-174.

[0021]

[0010] Roh, H. T., & Lee, J. W. (2012). Joint relay node placement and node scheduling in wireless networks with a relay node with controllable mobility. Wireless Communications and Mobile Computing, 12(8), 699-712.

[0022] Description of The Invention:

[0023] The invention to realize all the objectives mentioned above and which will emerge from the detailed description below; it provides the best positioning / placement of relaying nodes in LoRa networks to increase the coverage area, and the communication performance while minimizing the number of deployed relays. The relaying nodes are placed such that they maintain reliable links with the gateway and end-devices whether they are inside or outside the coverage area of the gateway.

[0024] This invention proposes an efficient positioning method for relaying nodes, which are external devices integrated into existing LoRa networks. Traditionally, a LoRa network comprises a gateway (or multiple gateways) and distributed sensors that employ singlehop transmission, i.e, employing a star topology for communication. The relaying nodes can function as either amplify and forward (AF) or decode and forward (DF) devices and are not limited to a one function. The placement of these relaying nodes is determined through an SNR / SIR maximization algorithm, taking into account co and cross-SF interference, as well as equal area-based (EAB) SF allocation for coverage distance slicing. By employing this algorithm, the optimal positioning of relaying nodes is achieved, ensuring efficient and reliable relaying links between the relay and gateway, and the relay and sensors. While minimizing the number of deployed relaying devices. The algorithm takes into account the impact of co-SF interference, which occurs when LoRa devices using the same spreading factor (SF) transmit simultaneously, as well as cross-SF interference, which arises when LoRa devices using different SFs transmit simultaneously.

[0025] The existing solutions primarily focus on selecting the best relaying nodes from a predefined set of nodes that are already deployed within the network. However, in this invention, the relays are considered as external devices, and their placement is determined based on the specific requirements and signal characteristics of each environment. This approach allows for greater flexibility and adaptability in the positioning of relaying nodes.

[0026] Most of the existing works and solutions revolve around employing multi-hop techniques for relaying nodes. However, in established LoRa networks, a star topology is commonly utilized, and deviating from this topology would require significant changes. In this invention, it was aimed to maintain the conventional star topology and transmission scheme, ensuring compatibility with existing LoRa networks. This invention objective is to efficiently position the relaying nodes to function as either DF or AF devices while minimizing the overall number of deployed relays. Without the frequent need to change the placement of these nodes each time period, ensuring stability and reliability in the network. The precise placement of the relays was determined through the utilization of an SNR / SIR maximization algorithm. This algorithm takes into consideration various factors, including the location of the gateway, sensors, and the coverage area. Additionally, it employes an EAB SF allocation approach to ensure optimal coverage and performance throughout the network. Co and cross-SF interferences are considered in the algorithm to increase the probability of success in the transmission.

[0027] The structural and characteristic features and all advantages of the method subject to the invention will be understood more clearly thanks to the figures given below and the detailed explanation written by making reference to these figures, and therefore the evaluation should be made by taking these figures and detailed explanation into consideration.

[0028] Description of the Figures:

[0029] The invention will be described with reference to the accompanying figures, so that the features of the invention will be more clearly understood and appreciated, but the purpose of this is not to limit the invention to these certain regulations. On the contrary, it is intended to cover all alternatives, changes and equivalences that can be included in the area of the invention defined by the accompanying claims. The details shown should be understood that they are shown only for the purpose of describing the preferred embodiments of the present invention and are presented in order to provide the most convenient and easily understandable description of both the shaping of methods and the rules and conceptual features of the invention. In these drawings;

[0030] Figure 1 Schematic view of the placement of the relay in established LoRa networks.

[0031] The figures to help understand the present invention are numbered as indicated in the attached image and are given below along with their names.

[0032] Disclosure of References:

[0033] 1. Gateway

[0034] 2. Relay

[0035] 3. Sensor

[0036] A. Coverage Area of The Gateway in Established Lora Networks

[0037] B. Extended Coverage Area

[0038] SF. Spreading Factor dg,r. The relay (2) - gateway (1) distance Description of The Invention:

[0039] The relays (2) serve as external devices integrated into the existing and well-established LoRa networks, which typically consist of a central gateway (1) surrounded by distributed sensors (3). These sensors (3) can be located both within and outside the coverage area of the gateway (1). The inclusion of relays (2) aims to extend the network's coverage and enhance communication performance. The relaying (2) nodes, acting as external devices, are introduced into the network to improve coverage and communication capabilities. These nodes can function as either DF or AF devices or both, depending on the specific requirements of the network. The placement of the relays (2) offers flexibility and freedom for optimal positioning. Unlike pre-defined and limited placement scenarios, the proposed approach allows for an optimum selection of relay (2) locations based on various factors such as signal strength, coverage area, and network requirements. The introduction of relaying (2) nodes does not result in any changes to the network topology of existing LoRa regulations. The network's established topology, typically characterized by a star configuration, remains unchanged, ensuring compatibility and seamless integration of the relays (2). Furthermore, the allocation of relays (2) is based on the principle of slicing the coverage area using an EAB-SF allocation. This allocation technique ensures efficient utilization of resources and optimal coverage distribution throughout the network. Then, an SNR / SIR maximization approach, employing a genetic algorithm, is proposed to efficiently allocate the relays (2). This algorithm optimizes the allocation process by maximizing SNR / SIR while simultaneously minimizing the number of deployed relaying (2) nodes. This enables an efficient and reliable relay (2) network configuration. While maximizing the SNR / SIR, the proposed algorithm of method considers reducing the co and cross-SF interference in the network to ensure low collision probabilities and thus increasing the throughput in the network.

[0040] The current solution that proposes a relay (2) node placement method in a LoRa network, is based on multi-hop mesh networks:

[0041] - Maximization problem is defined to find the optimum relaying (2) node position, relay (2) SF region along with the relay (2) to EDs ratio in the network, - Defining the best routing branch for each relayed (2) node based on its RS SI measurements,

[0042] Clustering the networks into multiple clusters and determining the cluster head as a relying node.

[0043] In a Lora network consisting of a gateway (1) and end devices, where the coverage area is sliced into discs based on EAB SF-allocation scheme a relay (2) placement is established based on the maximization of the network throughput, average success probability and coverage probability. The relay (2) is an external device placed in a position that ensures for the signals that are either directly forwarded or decoded than forwarded to exceed the SNR receiver threshold along with co-SF and cross-SF interference threshold when it reaches the gateway (1).

[0044] The placement is based on the derivation of the SNR and SIR success probabilities at the relay (2). A detailed mathematical derivation for SIR success probability is shown in the equation below to minimize the co-SF and cross-SF interferences for the signals broadcasted to the relay (2). aSTis the (end device (ED) regulated maximum duty cycle transmission for each spreading factor (SF)

[0045] A is the intensity of the Poisson Point Process (PPP) ds-F / dST-is the disc region of the specified spreading factor (SF) psiris the SIR threshold at the receiver

[0046] Z(tZ ) is the Path loss at distance d

[0047] A joint average success probability derived from the joint SNR and SIR success probabilities as a function of the relay (2) node distance from the gateway (1) and it position with the SF region related to other transmitting end devices. The average success probability is derived for the gateway (1) and relay (2) as indicated in the following equation.

[0048] Otg is the gateway (1) (GW) regulated maximum duty cycle transmission for each spreading factor (SF) cr2is the variance of the additive white gaussian noise

[0049] Ysr is reciever RSSI sensitivity for each spreading factor (SF)

[0050] T is the received signal RSSI threshold at the relay (2) where it is decided to be forwarded or not is the Path loss at distance d for node j outside coverage area is the Path loss at distance d for node i inside coverage area

[0051] A maximization problem is formulated to get the optimal relay (2) position that maximizes the average success probability and coverage probability as a function of the relay (2) - gateway (1) distance (dg,r) and relay (2)-end devices. To solve the previous developed problem a non-sorted genetic algorithm (Non-Dominated Sorting Genetic Algorithm -NSGA-II) is established to find the optimum relay (2) position where the following derived fitness functions can converge

[0052] Psf is the probabilitty of success for each spreading factor (SF) at the realy is the probabilitty of success for each spreading factor (SF) at the gateway (1) (GW) is the weighting factor for the optimization algorithm

[0053] (Pzkis the average coverage probability for the realy

[0054] (Pzkis the average coverage probaility for the gateway (1) (GW)

[0055] The defined maximization problem incorporates a continuous search variable (relay (2) position) which allow the extraction of the most optimum relay (2) position within the gateway (1) coverage are that ensures maximum system performance, unlike discrete search variable that ignores all the search space possibilities.

[0056] The relay (2) placement ensures connecting maximum number end devices which are placed outside the gateway (1) coverage area. The relay (2) acts as a gateway (1) for those end devices which allow the establishment of an extended coverage area sliced also into disc regions based on EAB SF-allocation scheme.

[0057] The foregoing descriptions of specific embodiments of the present technology have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present technology to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the present technology and its practical software, to thereby enable others skilled in the art to best utilize the present technology and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the software or implementation without departing from the spirit or scope of the claims of the present technology.

[0058] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fail within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

CLAIMS1- The invention is related to relaying node positioning method based on SIR / SNR maximization in LoRa networks, its feature is; placing of at least one relay (2) to extend the coverage area in a LoRa network with a central gateway (1) covering the area and where sensors (3) are distributed either inside or outside this area, slicing of the coverage area using an equal area based (EAB) spreading factor (SF) allocation technique, determining of the position of the external relay (2) node based on SNR, co- SF and cross SF thresholds, processing the transmitted signals to determine the signal-to-interference (SIR) / signal-to-noise (SIR) success probabilities at the relaying (2) node, defining the relay (2) placement as a maximization problem for success probability, coverage probability and network throughput, it includes the process steps of implementing an unsorted genetic algorithm to find the convergence of the defined maximization problem.2- The method according to claim 1, it is characterized in that it comprises the process step of defining a maximization problem for a system to obtain an optimal relay (2) position that maximizes the average success probability and coverage probability as a function of the relay (2)-gateway (1) distance, (dg,r) and the relay (2)-end devices (inside or / and outside) distances.3- The method according to claim 1, characterized in that it comprises the step of applying a non-sorted genetic algorithm to solve SNR / SIR maximization problem which finds the optimal relay (2) position to which the derived fitness functions can converge.

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