The LoRa communication standard allows to transmit data in radio networks over distances of few kilometers. Although the LoRa WAN protocol assumes the use of ALOHA channel access, due to the use of multiple channels and multiple spreading factors in LoRa networks the evaluation of collision probability is challenging. We present the simulation model allowing to estimate the per node probability of successful packet delivery ratio. The model is evaluated for different network topologies, based on random distribution of nodes or based on the real location of meters in sample smart city deployments. The results show that the packet delivery ratio varies significantly depending on the location of the end nodes in the network. Due to the higher spreading factor used, the collision probability is very low for nodes located near to the access point. The use of proposed simulation model allows to identify the nodes which experience high collision probability and react e.g. by reconfiguration.
a978-3-030-21952-9 a978-3-030-21951-2 00307nas a2200121 4500000000100000000000100001008004100002100002200043700001900065700001800084700001700102245006600119 2019 d 1 aKrzysztof Grochla 1 aRafał Marjasz 1 aKonrad Połys 1 aAnna Strzoda 00 aKolizje pakietów w sieciach LoRa w zastosowaniach smart city 01441nas a2200205 4500000000100000000000100001008004100002260002200043100002200065700001400087700002200101700002700123700002200150700001800172700002500190700001700215245008000232856003700312520088600349 2020 d c01/2021 bSpringer 1 aArtur Frankiewicz 1 aAdam Glos 1 aKrzysztof Grochla 1 aZbigniew Łaskarzewski 1 aJaroslaw Miszczak 1 aKonrad Połys 1 aPrzemysław Sadowski 1 aAnna Strzoda 00 aLP WAN Gateway Location Selection Using Modified K-Dominating Set Algorithm uhttps://arxiv.org/abs/2010.04553 3 aThe LP WAN networks use gateways or base stations to communicate with devices distributed on large distances, up to tens of kilometres. The selection of optimal gateway locations in wireless networks should allow providing the complete coverage for a given set of nodes, taking into account the limitations, such as the number of nodes served per access point or required redundancy. In this paper, we describe the problem of selecting the base stations in a network using the concept of k-dominating set. In our model, we include information about the required redundancy and spectral efficiency. We consider the additional requirements on the resulting connections and provide the greedy algorithm for solving the problem. The algorithm is evaluated in randomly generated network topologies and using the coordinates of sample real smart metering networks.
01795nas a2200181 4500000000100000000000100001008004100002260002700043100001900070700001700089700001800106700002200124245006500146856011600211300001200327520125200339022002201591 2021 d c10/2021 bEurosis aRome 1 aRafał Marjasz 1 aAnna Strzoda 1 aKonrad Połys 1 aKrzysztof Grochla 00 aMitigation of LoRa interferences via dynamic channel weights uhttps://www.researchgate.net/publication/356041519_Mitigation_of_LORA_Interferences_via_Dynamic_Channel_Weights a150-154 3 aThe Low Power Wide Area Networks, such as LoRa (Long Range), allow communication over a large area with limited bandwidth and small data rates, using an unlicensed band. LoRa uses multiple channels, but the number of channels available is relatively narrow - e.g. 8 in most countries in Europe. The transmission is subject to interferences due to many other devices transmitting within the same frequencies. In this paper, we evaluate how strong the interferences' influence is by analysing the packet delivery rate in a large scale LoRa network. The measurements have been made over more than seven months and show noticeable variability in packet delivery rate per channel both in space and in time. We propose to estimate the scale of interferences through continuous observation of the number of delivered packets for every channel per LoRa gateway or per selected area. We also discuss how this estimation may mitigate the interferences by assigning the dynamic weights to the LoRa channel selection procedure. We created an algorithm that periodically updates the weights, and a simulation model to evaluate the algorithm. A significant improvement in the efficiency of package delivery is visible, from 12% to 49% less packet loss.
a978-9-492-85918-1 01878nas a2200169 4500000000100000000000100001008004100002260005900043100001900102700001800121700001700139700002200156245004500178856006700223520140000290020001801690 2021 d bAssociation for Computing Machinery aNew York, NY, USA 1 aRafał Marjasz 1 aKonrad Połys 1 aAnna Strzoda 1 aKrzysztof Grochla 00 aImproving Delivery Ratio in LoRa Network uhttps://dl.acm.org/doi/10.1145/3479241.3486698?cid=99659913397 3 aThe LoRa (Long Range) radio communication provides large coverage areas, low bandwidth, small data rates, and transmitting limited data sizes, with very low energy usage. Multiple transmission channels are used, but the number of channels available is fairly limited - e.g. 8 in most countries in Europe. The amount of interferences may be estimated by the continuous observation of the number of delivered packets and the received signal strength. We propose to aggregate and analyse those metrics per channel and geographical area. When the proportion of received packets per some of the areas decreases, it is subjected to heavier interferences than other parts of the network and should be used less intensively. Thus we propose to allocate the weights for each channel per geographic area and/or device, defining the probability of the channel usage. Furthermore, we created an algorithm that periodically updates the weights and a simulation model using OMNeT++, FloRa, taking into account possible packets collisions. The algorithm has been validated for both random and realistic network topologies. The simulation evaluations show a significant improvement in the package delivery efficiency, by almost 50% in scenarios with very high interference level simulated and by more than 13% in scenarios fed with real-life measurements of packet delivery probability changes in time.
a9781450390798 01607nas a2200193 4500000000100000000000100001008004100002260001200043100002200055700001700077700001900094700002300113700002000136700002700156245006100183856005900244490000700303520110300310 2022 d c11/2022 1 aKrzysztof Grochla 1 aAnna Strzoda 1 aRafał Marjasz 1 aPrzemysław Głomb 1 aKamil Książek 1 aZbigniew Łaskarzewski 00 aEnergy-Aware Algorithm for Assignment of Relays in LPWAN uhttps://dl.acm.org/doi/10.1145/3544561?cid=99659913397 0 v18 3 aThe Low Power Wide Area Networks allow maintaining connectivity with devices over a distance of a few kilometres. However, due
to the presence of obstacles the communication range in an urban environment may be shorter, which creates multiple blind spots or
areas with limited coverage. The nodes acting as relays may solve this problem by forwarding the data from other devices with poor
connectivity. We propose and investigate a novel algorithm that assigns devices to the relay role while keeping the battery constraints.
We define the algorithm, describe the simulation testbed, and present performance evaluation in several scenarios, including real-life
topologies of IoT networks. The proposed relay selection scheme allows increasing the lifetime of the network by selecting the relay
taking into account the current battery capacity. Additionally, it outperforms a previously proposed relay selection algorithm for LP
WAN, showing an average 4% decrease in transmission time and an order of magnitude lower computation complexity.
The Bluetooth Low Energy (BLE) advertisement messages allow estimating the location and distance between mobile devices. We analyse how the signal level varies depending on interferences, distance and the orientation of the transmitting and receiving devices. To provide a representative BLE Received Signal Strength Index (RSSI) dataset, we present a series of experimental measurements, including different receiver antenna orientations for several distances. We analyse the signal level variability as a function of distance and angle in an indoor environment. Our measurements show that the mean RSSI value equals -65.5 dBm, -78.4 dBm, -84.1 dBm and -90.8 dBm for 1m, 5m, 10m, and 16m, respectively. The standard deviation changes from 5.7 dBm through 5.9 dBm, 4.5 dBm to 3.5 dBm for 1m, 5m, 10m, and 16m, respectively. We also investigate the influence of the interference, emulating environment in which devices actively transmit BLE signals with varying intensities; on average, at every 0ms (no additional signals), 4ms and 8ms. The experiments show that from 13% to 40% fewer packets are delivered per second in the presence of interferences. Nevertheless, the RSSI characteristics remain very similar regardless of the presence of emulated traffic or not.
a9781450394826 01556nas a2200157 4500000000100000000000100001008004100002260006800043100001700111700001900128700002200147245006200169856006700231520108200298020001801380 2022 d c10/2022 bAssociation for Computing Machinery aNew York, NY, USA 1 aAnna Strzoda 1 aRafał Marjasz 1 aKrzysztof Grochla 00 aHow Accurate is LoRa Positioning in Realistic Conditions? uhttps://dl.acm.org/doi/10.1145/3551662.3561260?cid=99659913397 3 aThe LoRa communication technology has been developed for data transmission, but recent research papers showed that it could also be used to estimate the transmitting devices' location. The location can be found with reasonable accuracy, with median error as low as tens of meters; however, in many cases, it is evaluated in very optimistic transmission conditions, e.g. using line-of-sight communication and on a small data set. We estimate how LoRa localization performs in a realistic scenario, using an extensive data set of a telemetric network of a few thousand devices to calculate the location using the trilateration and estimate the error of the calculation. Within the analysed dataset the true-range multilateration provides highly inaccurate location estimation, with a median error of 588.64 m and an average error of 808.57 m. We argue that the LoRa localization in real-life conditions is ineffective, as the estimation of the distance between the node and the gateway changes heavily depending on the location of the node and radio channel attenuation.
a9781450394826 01594nas a2200145 4500000000100000000000100001008004100002260005400043100001700097700001900114700002200133245006800155856004700223520117800270 2022 d c12/2022 bInfocommunications Journal aNice, France 1 aAnna Strzoda 1 aRafał Marjasz 1 aKrzysztof Grochla 00 aLoRa Positioning in Verification of Location Data's Credibility uhttps://www.infocommunications.hu/2022_4_8 3 aThe LoRa is a novel radio communication technology
providing low power and a high range of data transmission.
The LoRa transmission may be used for a low-cost localization
to estimate the network nodes’ location. Some recent research
showed that the location could be found with reasonable accuracy,
with median error as low as tens of meters. Still, such results are
achieved in a controlled environment with low interferences. We
first evaluate the LoRa localization using an extensive data set
of a telemetric network of a few thousand devices. We show that
although the direct positioning based on trilateration provides
limited accuracy, the measurement of LoRa transmission may
be successfully used to evaluate the credibility of location information.
The information about which gateways received the data
and the RSSI measurements allow us to verify if the potential
coordinates of a location are accurate. We propose a metric for
location verification and estimate its credibility on a sample of
measurements from the LoRa telemetry network.
Despite the ability of Low-Power Wide-Area Networks to offer extended range, they encounter challenges with coverage blind spots in the network. This article proposes an innovative energy-efficient and nature-inspired relay selection algorithm for LoRa-based LPWAN networks, serving as a solution for challenges related to poor signal range in areas with limited coverage. A swarm behavior-inspired approach is utilized to select the relays’ localization in the network, providing network energy efficiency and radio signal extension. These relays help to bridge communication gaps, significantly reducing the impact of coverage blind spots by forwarding signals from devices with poor direct connectivity with the gateway. The proposed algorithm considers critical factors for the LoRa standard, such as the Spreading Factor and device energy budget analysis. Simulation experiments validate the proposed scheme’s effectiveness in terms of energy efficiency under diverse multi-gateway (up to six gateways) network topology scenarios involving thousands of devices (1000–1500). Specifically, it is verified that the proposed approach outperforms a reference method in preventing battery depletion of the relays, which is vital for battery-powered IoT devices. Furthermore, the proposed heuristic method achieves over twice the speed of the exact method for some large-scale problems, with a negligible accuracy loss of less than 2%.
02261nas a2200169 4500000000100000000000100001008004100002260001200043100001800055700001900073700001700092245007800109856005000187490000700237520183300244022001402077 2025 d c07/2025 1 aNur Kelesoglu 1 aMarzena Halama 1 aAnna Strzoda 00 aEnhancing LoRa-based Outdoor Localization Accuracy Using Machine Learning uhttps://ieeexplore.ieee.org/document/11080010 0 v13 3 aThe Internet of Things is gaining significant relevance, driving increasing interest in location-
based services using wireless signals, particularly Low Power Wide Area Network (LPWAN) technology.
LoRa (Long Range), together with LoRaWAN, is a prominent LPWAN standard that provides long-range
connectivity and low energy consumption, making it viable for IoT-based positioning systems in smart
cities. For localization systems leveraging LoRa signals, Machine Learning (ML) approaches are being
increasingly explored, as ML-based solutions offer a powerful way to enhance the accuracy of positioning.
In this study, we propose various ML approaches for LoRa-based positioning in outdoor environments. We
evaluate six different ML models: k-NN, CNN, SVR, ANN, XG-Boost, and LightGBM-using an open-
source urban LoRaWAN dataset. We further propose a Hybrid Model that combines convolutional feature
extraction with gradient-boosted regression. This architecture integrates the strengths of deep learning and
tree-based models, aiming to capture both temporal signal patterns and structured input correlations for
improved localization accuracy. The models are trained offline and tested for performance in terms of
localization accuracy, mean square error, and computational efficiency. Additionally, we investigate the
impact of different Feature Vector (FV) subsets on localization performance by analyzing the significance of
LoRaWAN signal attributes. Our results highlight the effectiveness of ML models in enhancing localization
accuracy for LoRa-based outdoor positioning systems, demonstrating performance improvements ranging
from 10% to 73% compared to previous ML studies in outdoor localization.
The CSMA mode of operation was recently added to the LoRaWAN standard. We investigate, using a queueing model, how the introduction of CSMA in the communication between devices and the gate in LoRaWan may increase the probability of successful transmission. In the proposed model, customers (transmissions) are served by a multichannel station (gate), and the number of channels corresponds to the number of transmissions that can be performed in parallel. We introduce a queueing model with multiple attempts to find a free channel and investigate their impact on transmission throughput and reliability.