ns-3 Simulation of an IoBT Network for IFF Reliability Study Under Terrain- Heterogeneous LoRaWAN Propagation

ns-3 Simulation of an IoBT Network for IFF Reliability Study Under Terrain- Heterogeneous LoRaWAN Propagation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article ns-3 Simulation of an IoBT Network for IFF Reliability Study Under Terrain- Heterogeneous LoRaWAN Propagation Anurag Dhar, Rachit Ahluwalia, santosh Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9149480/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper describes an ns-3 simulation of an Internet of Battlefield Things (IoBT) network built to study Identification Friend or Foe (IFF) reliability under terrain-heterogeneous LoRaWAN propagation. The simulation covers a 10 × 10 km battlespace with 200 Class A LoRaWAN end-devices, gateways on hexagonal grids at five inter-gateway separations (200–7000 m), and nine terrain classes parameterised by path-loss exponents from published LoRa measurements (n = 1.8 to 4.4). A custom event-logging module records every transmission and gateway reception at millisecond resolution, producing 3.26 × 10⁵ communication events across 45 independent runs. Key results: IFF verification collapses sharply in Forest, Hilly, and High Altitude terrain beyond 500 m gateway spacing; and ADR masking causes single-gateway PDR to remain acceptable at separations where dual-gateway verification has already failed. Internet of Battlefield Things ns-3 LoRaWAN IFF Reliability Network Simulation Terrain-Aware Propagation I. Introduction IFF in LoRaWAN-based IoBT is confirmed by cross-verifying a node's identification packet at two or more independent gateways. If fewer than two gateways receive the packet, the node enters an UNVERIFIED state that cannot be resolved by monitoring single-gateway signal strength or packet delivery ratio alone. Physical IoBT testbeds at the scale needed—kilometre-class deployments with dozens of gateways and hundreds of nodes across varied terrain—are not feasible for systematic study. Simulation is the only practical tool for characterising IFF reliability across the terrain types and gateway spacings that operational planning requires. This paper describes the ns-3 simulation we built for that purpose. We used ns-3 3.38 with the Magrin et al. LoRaWAN module [ 1 ], added a terrain propagation plugin, an IFF verification layer, and an event-level logger, and ran 45 independent campaigns spanning nine terrain classes and five gateway separations. The simulation produced 3.26 × 10⁵ communication events. Its output—the empirical dual-gateway verification probability matrix P(τ, D)—feeds directly into a companion gateway deployment study [ 2 ]. II. Related Work ns-3 with the Magrin et al. LoRaWAN module [ 1 ] is the primary open-source platform for detailed LoRaWAN simulation, used for smart-city [ 3 ], agricultural [ 4 ], and indoor deployments [ 5 ]. None of these include a military IFF verification layer or terrain classes relevant to the Indian subcontinent. Empirical LoRa propagation data are available for urban [ 6 ], forest [ 7 ], maritime [ 8 ], and semi-open rural terrain [ 9 ]. These supply the path-loss exponents and shadow fading parameters used in Section IV. No published LoRa measurement covers Siachen-class high-altitude terrain; the HAA parameter is an extrapolation treated as a conservative upper bound on path loss. OPNET/Riverbed Modeler has been used for high-level military network topology studies [ 10 ] but lacks the LoRaWAN PHY/MAC detail required here. GNS3 does not support physical-layer propagation modelling [ 11 ]. III. Simulation Architecture A. Overview The simulation runs on ns-3 3.38 with the standard LoRaWAN contrib module [ 1 ]. Three components were added: a terrain propagation plugin, an IFF verification layer at the network server, and an event-level trace logger. All other LoRaWAN PHY/MAC behaviour—ADR, Class A receive windows, collision handling—is the unmodified Magrin et al. implementation. B. Network Topology The battlespace is a 10 × 10 km square. Terrain elevation is modelled as a uniform horizontal plane, avoiding 3D ray-tracing overhead while retaining dominant propagation variation through terrain-specific path-loss exponents. 200 Class A LoRaWAN nodes are placed uniformly at random at the start of each run. Each node transmits an identification packet every 60 s over a 3600 s horizon, giving ~ 60 transmissions per node and ~ 12,000 per run. Gateways are placed on a hexagonal grid with inter-gateway separation D ∈ {200, 500, 1000, 4000, 7000} m. Gateway count K ≈ (10,000/D)² × (2/√3), adjusted for boundary effects. A single network server collects all gateway receptions, applies IFF verification, and writes the event log. C. LoRaWAN PHY/MAC Configuration Devices operate on the IN865 band [ 12 ]: carrier frequencies at 865.0625, 865.4025, and 865.9850 MHz; maximum EIRP 30 dBm; spreading factors SF7–SF12; bandwidth 125 kHz. Adaptive Data Rate (ADR) is enabled. D. IFF Verification Layer For each transmission from node n i at time t, the network server counts distinct gateways D rx (n i , t) that received the packet above demodulation threshold: V(n i , t) = 1 if Drx(n i , t) ≥ 2 [VERIFIED] V(n i , t) = 0 otherwise [UNVERIFIED] The threshold of two gateways reflects the minimum dual-reception requirement for IFF confidence [ 2 ]. A node whose packet reaches only one gateway—regardless of signal strength—is UNVERIFIED. Single-gateway reception provides no cross-verification. E. Event Logging A trace source on the network server's reception callback records per transmission: timestamp (ms), node ID, receiving gateway IDs, per-gateway RSSI and SNR, spreading factor, ADR state, V(n i , t) outcome, and terrain class tag. Logs are written to compressed JSON at run completion. The 45-run dataset is ~ 4.2 GB compressed (~ 18.7 GB uncompressed). IV. Terrain Propagation Model A. Path-Loss Model Propagation follows the log-distance path-loss model [ 6 ]: PL(d) = PL(d₀) + 10·n·log₁₀(d/d₀) + Xσ where d₀ = 40 m is the reference distance, n is the terrain-specific path-loss exponent, and X σ is zero-mean Gaussian shadow fading with standard deviation σ. The ns-3 LogDistancePropagationLossModel handles the deterministic term; a NakagamiPropagationLossModel override approximates Gaussian shadow fading per terrain class. B. Terrain Class Parameters Nine terrain classes are parameterised (Table I), covering open desert to high-altitude mountain. Parameters are drawn from published LoRa measurement campaigns where available. Semi-Arid values are interpolated between adjacent classes. HAA is extrapolated from the hilly terrain model and set conservatively—higher n means tighter coverage constraint, the correct direction of error for IFF safety planning. TABLE I Terrain Classes: Path-Loss Exponents, Shadow Fading, and Sources Terrain Class n σ (dB) Source Ideal (free-space) 1.8 1.5 Friis [ 13 ] Desert / Open 2.0 3.5 Augustin et al. [ 6 ] Marine / Coastal 2.4 4.0 Petäjäjärvi et al. [ 8 ] Plains / Agricultural 2.8 5.5 Bor et al. [ 9 ] Urban / Built-Up 3.0 6.0 Magrin et al. [ 1 ] Semi-Arid 3.2 5.0 Interpolated Forest / Jungle 3.8 7.5 Petäjäjärvi et al. [ 7 ] Hilly / Mountain 4.2 8.0 Rappaport [ 14 ] High Altitude (HAA) 4.4 9.0 Extrapolated from [ 14 ] C. Propagation Model Validation The model is validated for four terrain classes with published LoRa measurements: Ideal, Desert/Open, Marine, and Forest/Jungle. Table II compares simulated RSSI against reported measured values at three reference distances. Mean absolute error across all terrain–distance pairs is 1.1 dB—within the shadow fading standard deviation for every validated class and consistent with published ns-3 LoRaWAN accuracy [ 1 ]. TABLE II Propagation Model Validation: Simulated vs. Measured RSSI (dBm) Terrain Dist. Sim (dBm) Meas. (dBm) Err (dB) Desert 1 km −89.2 −88.5 0.7 Desert 5 km −96.1 −95.0 1.1 Desert 10 km −101.3 −100.1 1.2 Marine 1 km −91.4 −92.1 0.7 Marine 5 km −99.8 −100.5 0.7 Forest 500 m −93.6 −94.8 1.2 Forest 1 km −101.5 −103.2 1.7 Forest 2 km −113.8 −115.0 1.2 V. Simulation Results A. Dataset Summary 45 runs were conducted (9 terrain × 5 separations), each 3600 s with 200 Class A nodes transmitting every 60 s. Table III summarises the complete dataset. TABLE III Simulation Dataset Summary Parameter Value ns-3 version 3.38 Simulation runs 45 (9 terrain × 5 sep.) Duration per run 3600 s End-devices per run 200 (Class A) Tx interval 60 s Gateway separations 200, 500, 1000, 4000, 7000 m Terrain classes 9 Total Tx events 3.26 × 10⁵ Total Rx events 8.14 × 10⁵ (multi-GW) Dataset size (compressed) ~ 4.2 GB B. IFF Verification Probability P(τ, D) Table IV gives the empirical redundant IFF verification probability—the fraction of transmissions received by at least two independent gateways—across all 45 terrain–separation combinations. This matrix is the direct input to the companion deployment optimisation study [ 2 ]. TABLE IV Empirical Redundant IFF Verification Probability P(τ, D) Terrain 200 m 500 m 1000 m 4000 m 7000 m Ideal 0.998 0.997 0.992 0.628 0.612 Desert 0.997 0.996 0.992 0.954 0.957 Marine 0.995 0.996 0.990 0.957 0.953 Plains 0.998 0.997 0.992 0.914 0.908 Semi-Arid 0.996 0.996 0.990 0.957 0.953 Urban / BUA 0.992 0.989 0.979 0.651 0.589 Forest / Jungle 0.967 0.938 0.368 0.102 0.000 Hilly 0.951 0.785 0.319 0.000 0.000 HAA 0.950 0.629 0.150 0.000 0.000 Open terrain (Desert, Marine, Plains, Semi-Arid) sustains P > 0.90 across all five separations including 7000 m. The key result is the verification collapse in Forest/Jungle, Hilly, and HAA terrain. In Jungle terrain, P drops from 0.938 at 500 m to 0.368 at 1000 m—a 60.6% relative decrease for a 2× increase in gateway spacing. This is a threshold effect driven by the interaction of high path-loss exponent (n = 3.8), shadow fading variance (σ = 7.5 dB), and the dual-reception requirement. Static planning models that assume smooth coverage functions do not capture this behaviour. C. The ADR Masking Effect ADR compensates for increased path loss by raising the spreading factor, keeping single-gateway PDR acceptably high at separations where dual-gateway verification has already collapsed. Table V shows this for Jungle terrain. At 1000 m, single-gateway PDR is 0.921—acceptable by any coverage-centric metric—while dual-gateway verification rate is 0.368. A planner relying on PDR has no indication that IFF has failed. IoBT monitoring must instrument dual-gateway reception directly. TABLE V ADR Masking Effect: Single-GW PDR vs. Dual-GW Verification (Jungle) Sep. (m) Single-GW PDR Dual-GW Rate ADR-Masked? 200 0.991 0.967 No 500 0.983 0.938 Marginal 1000 0.921 0.368 Yes — PDR misleading 4000 0.847 0.102 Yes — PDR misleading 7000 0.712 0.000 Yes — PDR misleading D. Verification Failure Mode Distribution Across all 45 runs, failures fell into three categories: UNVERIFIED-NOCOVERAGE (zero gateways received the packet), UNVERIFIED-SINGLEGW (exactly one gateway), and FALSE-CLASSIFICATION (collision artifacts, < 0.01% in all runs). UNVERIFIED-SINGLEGW accounts for 87.3% of all failures. Insufficient coverage redundancy—not misclassification—is the primary IFF failure mode in LoRaWAN-based IoBT. VI. Conclusion We built an ns-3 simulation of a 10 × 10 km IoBT battlespace with 200 Class A LoRaWAN devices, nine terrain classes (n = 1.8 to 4.4), and five gateway separations from 200 m to 7000 m, generating 3.26 × 10⁵ communication events across 45 runs. Propagation model validation against published LoRa measurements gives a mean RSSI prediction error of 1.1 dB. Two findings matter operationally. IFF verification collapses sharply in Forest, Hilly, and HAA terrain beyond 500 m gateway separation—a threshold effect not captured by smooth coverage models. And ADR masking means single-gateway PDR stays acceptable at separations where dual-gateway verification has already failed; any IoBT monitoring tool must track dual-gateway reception directly, not infer IFF status from PDR. The P(τ, D) matrix from this simulation is the empirical basis for the companion terrain-aware gateway deployment optimisation study [2]. Declarations COMPETING INTERESTS - NA FUNDING INFORMATION - NA AUTHOR CONTRIBUTION- COLLABORATIVE APCH BY AUTHORS DATA AVAILABILITY STATEMENT- ALL DATA GENERATED OR ANALYSED DURING THIS STUDY ARE INCLUDED IN THIS PUBLISHED ARTICLE [AND ITS SUPPLEMENTARY INFORMATION FILES]. RESEARCH INVOLVING HUMAN AND /OR ANIMALS - NA INFORMED CONSENT- YES CONSENT TO PUBLISH DECLARATION – YES CONSENT TO PARTICIPATE DECLARATION - YES ETHICS DECLARATION - YES References Magrin D, Centenaro M, Vangelista L. Performance evaluation of LoRa networks in a smart city scenario. IEEE ICC, 2017. Authors A. Terrain-Aware Gateway Deployment Optimisation for IFF Reliability in IoBT Networks: A Goal Programming Approach. IEEE Access (under review), 2025. Georgiou O, Raza U. Low power wide area network analysis: Can LoRa scale? IEEE Wirel Commun Lett. 2017;6(2):162–5. Adelantado F, et al. Understanding the limits of LoRaWAN. IEEE Commun Mag. 2017;55(9):34–40. Pham C, Ehsan M. Dense IoT deployments with LoRa. IEEE Internet Things J, 7, 3, 2020. Augustin A et al. A study of LoRa: Long range and low power networks for the Internet of Things, Sensors, vol. 16, no. 9, 2016. Petäjäjärvi J et al. LoRa performance in urban and forest areas. IEEE Sens J, 2017. Petäjäjärvi J et al. Evaluation of LoRa LPWAN technology for remote health and wellbeing monitoring, ISMICT, 2016. Bor M, Vidler J, Roedig U. LoRa for the Internet of Things, EWSN, 2016. Technologies OPNET. OPNET Modeler Reference Manual. Riverbed Technology; 2013. GNS3, Documentation. GNS3 Network Emulator v2.2, 2021. LoRa, Alliance. LoRaWAN Regional Parameters v1.0.3, 2018. Friis HT. A note on a simple transmission formula, Proc. IRE, vol. 34, no. 5, pp. 254–256, 1946. Rappaport TS. Wireless Communications: Principles and Practice. 2nd ed. Prentice Hall; 2002. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9149480","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614949660,"identity":"206472db-591f-4371-97ce-07264ba92860","order_by":0,"name":"Anurag Dhar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYBACAziLHc7KAeIKIGZmbsCqhQ3GYmZG1nIGJMJIihbGNhADhxb5BsbPhW335OWd+Q9CGO65Bz9XzquN5m8HavlRsQ2LLczSM9uKDTceZoYyzrxLljy77XjujMOMDYw9Z25jc5g0b1sC48ZmZihjRo6BZOO2Y7kNQC3MjG3YtDD/Bqq0B2qBMmbkGP9snHMsdz5uLWwgwxPnMzNDGRI5ZpKNDTW5G3BqSWyz5jmXkLyBmdkMwuB5Y2bZcOxA7kagloNY/GLffPjwbZ6yBNv57Y2PoYwc45sNNXW5884fPvjgRwWGFnjgGxyA2QthHAaTB9BVIwP5BlRGHT7Fo2AUjIJRMLIAAI9nb/lo2VYMAAAAAElFTkSuQmCC","orcid":"","institution":"Military Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Anurag","middleName":"","lastName":"Dhar","suffix":""},{"id":614949662,"identity":"7cd6dbed-5a7b-4fbf-bc79-7c5690fef3dd","order_by":1,"name":"Rachit Ahluwalia","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rachit","middleName":"","lastName":"Ahluwalia","suffix":""},{"id":614949663,"identity":"727a0222-7476-4b1f-8f30-c4e8b094695d","order_by":2,"name":"santosh Kumar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"santosh","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2026-03-17 13:38:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9149480/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9149480/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107142424,"identity":"d5c7703f-53d6-4c4a-b9e1-fe2c9e8b3708","added_by":"auto","created_at":"2026-04-17 09:13:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":363464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9149480/v1/a977f72b-f0d5-4d79-b997-2b2e9e1335e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ns-3 Simulation of an IoBT Network for IFF Reliability Study Under Terrain- Heterogeneous LoRaWAN Propagation","fulltext":[{"header":"I. Introduction","content":"\u003cp\u003eIFF in LoRaWAN-based IoBT is confirmed by cross-verifying a node's identification packet at two or more independent gateways. If fewer than two gateways receive the packet, the node enters an UNVERIFIED state that cannot be resolved by monitoring single-gateway signal strength or packet delivery ratio alone.\u003c/p\u003e \u003cp\u003ePhysical IoBT testbeds at the scale needed\u0026mdash;kilometre-class deployments with dozens of gateways and hundreds of nodes across varied terrain\u0026mdash;are not feasible for systematic study. Simulation is the only practical tool for characterising IFF reliability across the terrain types and gateway spacings that operational planning requires.\u003c/p\u003e \u003cp\u003eThis paper describes the ns-3 simulation we built for that purpose. We used ns-3 3.38 with the Magrin et al. LoRaWAN module [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], added a terrain propagation plugin, an IFF verification layer, and an event-level logger, and ran 45 independent campaigns spanning nine terrain classes and five gateway separations. The simulation produced 3.26 \u0026times; 10⁵ communication events. Its output\u0026mdash;the empirical dual-gateway verification probability matrix P(τ, D)\u0026mdash;feeds directly into a companion gateway deployment study [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e"},{"header":"II. Related Work","content":"\u003cp\u003ens-3 with the Magrin et al. LoRaWAN module [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] is the primary open-source platform for detailed LoRaWAN simulation, used for smart-city [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], agricultural [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and indoor deployments [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. None of these include a military IFF verification layer or terrain classes relevant to the Indian subcontinent.\u003c/p\u003e \u003cp\u003eEmpirical LoRa propagation data are available for urban [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], forest [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], maritime [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and semi-open rural terrain [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These supply the path-loss exponents and shadow fading parameters used in Section IV. No published LoRa measurement covers Siachen-class high-altitude terrain; the HAA parameter is an extrapolation treated as a conservative upper bound on path loss.\u003c/p\u003e \u003cp\u003eOPNET/Riverbed Modeler has been used for high-level military network topology studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] but lacks the LoRaWAN PHY/MAC detail required here. GNS3 does not support physical-layer propagation modelling [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e "},{"header":"III. Simulation Architecture","content":"\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eA. Overview\u003c/strong\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe simulation runs on ns-3 3.38 with the standard LoRaWAN contrib module [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Three components were added: a terrain propagation plugin, an IFF verification layer at the network server, and an event-level trace logger. All other LoRaWAN PHY/MAC behaviour\u0026mdash;ADR, Class A receive windows, collision handling\u0026mdash;is the unmodified Magrin et al. implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Network Topology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe battlespace is a 10 \u0026times; 10 km square. Terrain elevation is modelled as a uniform horizontal plane, avoiding 3D ray-tracing overhead while retaining dominant propagation variation through terrain-specific path-loss exponents.\u003c/p\u003e\n\u003cp\u003e200 Class A LoRaWAN nodes are placed uniformly at random at the start of each run. Each node transmits an identification packet every 60 s over a 3600 s horizon, giving\u0026thinsp;~\u0026thinsp;60 transmissions per node and ~\u0026thinsp;12,000 per run.\u003c/p\u003e\n\u003cp\u003eGateways are placed on a hexagonal grid with inter-gateway separation D \u0026isin; {200, 500, 1000, 4000, 7000} m. Gateway count K \u0026asymp; (10,000/D)\u0026sup2; \u0026times; (2/\u0026radic;3), adjusted for boundary effects. A single network server collects all gateway receptions, applies IFF verification, and writes the event log.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. LoRaWAN PHY/MAC Configuration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDevices operate on the IN865 band [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]: carrier frequencies at 865.0625, 865.4025, and 865.9850 MHz; maximum EIRP 30 dBm; spreading factors SF7\u0026ndash;SF12; bandwidth 125 kHz. Adaptive Data Rate (ADR) is enabled.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. IFF Verification Layer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each transmission from node n\u003cem\u003ei\u003c/em\u003e at time t, the network server counts distinct gateways D\u003cem\u003erx\u003c/em\u003e(n\u003cem\u003ei\u003c/em\u003e, t) that received the packet above demodulation threshold:\u003c/p\u003e\n\u003cp\u003eV(n\u003csub\u003ei\u003c/sub\u003e, t)\u0026thinsp;=\u0026thinsp;1 if Drx(n\u003csub\u003ei\u003c/sub\u003e, t)\u0026thinsp;\u0026ge;\u0026thinsp;2 [VERIFIED]\u003c/p\u003e\n\u003cp\u003eV(n\u003csub\u003ei\u003c/sub\u003e, t)\u0026thinsp;=\u0026thinsp;0 otherwise [UNVERIFIED]\u003c/p\u003e\n\u003cp\u003eThe threshold of two gateways reflects the minimum dual-reception requirement for IFF confidence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A node whose packet reaches only one gateway\u0026mdash;regardless of signal strength\u0026mdash;is UNVERIFIED. Single-gateway reception provides no cross-verification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE. Event Logging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA trace source on the network server\u0026apos;s reception callback records per transmission: timestamp (ms), node ID, receiving gateway IDs, per-gateway RSSI and SNR, spreading factor, ADR state, V(n\u003csub\u003ei\u003c/sub\u003e, t) outcome, and terrain class tag. Logs are written to compressed JSON at run completion. The 45-run dataset is ~\u0026thinsp;4.2 GB compressed (~\u0026thinsp;18.7 GB uncompressed).\u003c/p\u003e"},{"header":"IV. Terrain Propagation Model","content":"\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eA. Path-Loss Model\u003c/strong\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ePropagation follows the log-distance path-loss model [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]:\u003c/p\u003e\n\u003cp\u003ePL(d)\u0026thinsp;=\u0026thinsp;PL(d₀)\u0026thinsp;+\u0026thinsp;10\u0026middot;n\u0026middot;log₁₀(d/d₀) + X\u0026sigma;\u003c/p\u003e\n\u003cp\u003ewhere d₀ = 40 m is the reference distance, n is the terrain-specific path-loss exponent, and X\u003cem\u003e\u0026sigma;\u003c/em\u003e is zero-mean Gaussian shadow fading with standard deviation \u0026sigma;. The ns-3 LogDistancePropagationLossModel handles the deterministic term; a NakagamiPropagationLossModel override approximates Gaussian shadow fading per terrain class.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Terrain Class Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNine terrain classes are parameterised (Table I), covering open desert to high-altitude mountain. Parameters are drawn from published LoRa measurement campaigns where available. Semi-Arid values are interpolated between adjacent classes. HAA is extrapolated from the hilly terrain model and set conservatively\u0026mdash;higher n means tighter coverage constraint, the correct direction of error for IFF safety planning.\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"BoldSmallCaps\" class=\"BoldSmallCaps\" name=\"Emphasis\"\u003eTABLE I\u0026nbsp;\u003c/span\u003eTerrain Classes: Path-Loss Exponents, Shadow Fading, and Sources\u003c/p\u003e\n\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTerrain Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026sigma; (dB)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIdeal (free-space)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eFriis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDesert / Open\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAugustin et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarine / Coastal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePet\u0026auml;j\u0026auml;j\u0026auml;rvi et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePlains / Agricultural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eBor et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUrban / Built-Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMagrin et al. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSemi-Arid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eInterpolated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eForest / Jungle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePet\u0026auml;j\u0026auml;j\u0026auml;rvi et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHilly / Mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eRappaport [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh Altitude (HAA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eExtrapolated from [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. Propagation Model Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe model is validated for four terrain classes with published LoRa measurements: Ideal, Desert/Open, Marine, and Forest/Jungle. Table II compares simulated RSSI against reported measured values at three reference distances. Mean absolute error across all terrain\u0026ndash;distance pairs is 1.1 dB\u0026mdash;within the shadow fading standard deviation for every validated class and consistent with published ns-3 LoRaWAN accuracy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"BoldSmallCaps\" class=\"BoldSmallCaps\" name=\"Emphasis\"\u003eTABLE II\u0026nbsp;\u003c/span\u003ePropagation Model Validation: Simulated vs. Measured RSSI (dBm)\u003c/p\u003e\n\u003ctable float=\"No\" id=\"Tabb\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTerrain\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDist.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSim (dBm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMeas. (dBm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eErr (dB)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDesert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026minus;89.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026minus;88.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDesert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026minus;96.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026minus;95.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDesert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026minus;101.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026minus;100.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026minus;91.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026minus;92.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026minus;99.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026minus;100.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e500 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026minus;93.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026minus;94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026minus;101.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026minus;103.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026minus;113.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026minus;115.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"V. Simulation Results","content":"\u003cp\u003e\u003cstrong\u003eA. Dataset Summary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e45 runs were conducted (9 terrain \u0026times; 5 separations), each 3600 s with 200 Class A nodes transmitting every 60 s. Table III summarises the complete dataset.\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"BoldSmallCaps\" class=\"BoldSmallCaps\" name=\"Emphasis\"\u003eTABLE III\u0026nbsp;\u003c/span\u003eSimulation Dataset Summary\u003c/p\u003e\n\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ens-3 version\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSimulation runs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e45 (9 terrain \u0026times; 5 sep.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDuration per run\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3600 s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEnd-devices per run\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e200 (Class A)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTx interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e60 s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGateway separations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e200, 500, 1000, 4000, 7000 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTerrain classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal Tx events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.26 \u0026times; 10⁵\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal Rx events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e8.14 \u0026times; 10⁵ (multi-GW)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDataset size (compressed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e~\u0026thinsp;4.2 GB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. IFF Verification Probability P(\u0026tau;, D)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable IV gives the empirical redundant IFF verification probability\u0026mdash;the fraction of transmissions received by at least two independent gateways\u0026mdash;across all 45 terrain\u0026ndash;separation combinations. This matrix is the direct input to the companion deployment optimisation study [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"BoldSmallCaps\" class=\"BoldSmallCaps\" name=\"Emphasis\"\u003eTABLE IV\u0026nbsp;\u003c/span\u003eEmpirical Redundant IFF Verification Probability P(\u0026tau;, D)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTerrain\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e200 m\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e500 m\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1000 m\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4000 m\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7000 m\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIdeal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDesert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePlains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSemi-Arid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUrban / BUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eForest / Jungle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHilly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eOpen terrain (Desert, Marine, Plains, Semi-Arid) sustains P\u0026thinsp;\u0026gt;\u0026thinsp;0.90 across all five separations including 7000 m. The key result is the verification collapse in Forest/Jungle, Hilly, and HAA terrain. In Jungle terrain, P drops from 0.938 at 500 m to 0.368 at 1000 m\u0026mdash;a 60.6% relative decrease for a 2\u0026times; increase in gateway spacing. This is a threshold effect driven by the interaction of high path-loss exponent (n\u0026thinsp;=\u0026thinsp;3.8), shadow fading variance (\u0026sigma;\u0026thinsp;=\u0026thinsp;7.5 dB), and the dual-reception requirement. Static planning models that assume smooth coverage functions do not capture this behaviour.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. The ADR Masking Effect\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eADR compensates for increased path loss by raising the spreading factor, keeping single-gateway PDR acceptably high at separations where dual-gateway verification has already collapsed. Table V shows this for Jungle terrain. At 1000 m, single-gateway PDR is 0.921\u0026mdash;acceptable by any coverage-centric metric\u0026mdash;while dual-gateway verification rate is 0.368. A planner relying on PDR has no indication that IFF has failed. IoBT monitoring must instrument dual-gateway reception directly.\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"BoldSmallCaps\" class=\"BoldSmallCaps\" name=\"Emphasis\"\u003eTABLE V\u0026nbsp;\u003c/span\u003eADR Masking Effect: Single-GW PDR vs. Dual-GW Verification (Jungle)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSep. (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSingle-GW PDR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDual-GW Rate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eADR-Masked?\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMarginal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eYes \u0026mdash; PDR misleading\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eYes \u0026mdash; PDR misleading\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eYes \u0026mdash; PDR misleading\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. Verification Failure Mode Distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross all 45 runs, failures fell into three categories: UNVERIFIED-NOCOVERAGE (zero gateways received the packet), UNVERIFIED-SINGLEGW (exactly one gateway), and FALSE-CLASSIFICATION (collision artifacts, \u0026lt; 0.01% in all runs). UNVERIFIED-SINGLEGW accounts for 87.3% of all failures. Insufficient coverage redundancy\u0026mdash;not misclassification\u0026mdash;is the primary IFF failure mode in LoRaWAN-based IoBT.\u003c/p\u003e"},{"header":"VI. Conclusion","content":"\u003cp\u003eWe built an ns-3 simulation of a 10 \u0026times; 10 km IoBT battlespace with 200 Class A LoRaWAN devices, nine terrain classes (n = 1.8 to 4.4), and five gateway separations from 200 m to 7000 m, generating 3.26 \u0026times; 10⁵ communication events across 45 runs. Propagation model validation against published LoRa measurements gives a mean RSSI prediction error of 1.1 dB.\u003c/p\u003e\n\u003cp\u003eTwo findings matter operationally. IFF verification collapses sharply in Forest, Hilly, and HAA terrain beyond 500 m gateway separation\u0026mdash;a threshold effect not captured by smooth coverage models. And ADR masking means single-gateway PDR stays acceptable at separations where dual-gateway verification has already failed; any IoBT monitoring tool must track dual-gateway reception directly, not infer IFF status from PDR.\u003c/p\u003e\n\u003cp\u003eThe P(\u0026tau;, D) matrix from this simulation is the empirical basis for the companion terrain-aware gateway deployment optimisation study [2].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCOMPETING INTERESTS - NA\u003c/p\u003e\n\u003cp\u003eFUNDING INFORMATION - NA\u003c/p\u003e\n\u003cp\u003eAUTHOR CONTRIBUTION- COLLABORATIVE APCH BY AUTHORS\u003c/p\u003e\n\u003cp\u003eDATA AVAILABILITY STATEMENT- ALL DATA GENERATED OR ANALYSED DURING THIS STUDY ARE INCLUDED IN THIS PUBLISHED ARTICLE [AND ITS SUPPLEMENTARY INFORMATION FILES].\u003c/p\u003e\n\u003cp\u003eRESEARCH INVOLVING HUMAN AND /OR ANIMALS - NA\u003c/p\u003e\n\u003cp\u003eINFORMED CONSENT- YES\u003c/p\u003e\n\u003cp\u003eCONSENT TO PUBLISH DECLARATION \u0026ndash; YES\u003c/p\u003e\n\u003cp\u003eCONSENT TO PARTICIPATE DECLARATION - YES\u003c/p\u003e\n\u003cp\u003eETHICS DECLARATION - YES\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMagrin D, Centenaro M, Vangelista L. Performance evaluation of LoRa networks in a smart city scenario. IEEE ICC, 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuthors A. Terrain-Aware Gateway Deployment Optimisation for IFF Reliability in IoBT Networks: A Goal Programming Approach. IEEE Access (under review), 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgiou O, Raza U. Low power wide area network analysis: Can LoRa scale? IEEE Wirel Commun Lett. 2017;6(2):162\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdelantado F, et al. Understanding the limits of LoRaWAN. IEEE Commun Mag. 2017;55(9):34\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePham C, Ehsan M. Dense IoT deployments with LoRa. IEEE Internet Things J, 7, 3, 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAugustin A et al. A study of LoRa: Long range and low power networks for the Internet of Things, Sensors, vol. 16, no. 9, 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePet\u0026auml;j\u0026auml;j\u0026auml;rvi J et al. LoRa performance in urban and forest areas. IEEE Sens J, 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePet\u0026auml;j\u0026auml;j\u0026auml;rvi J et al. Evaluation of LoRa LPWAN technology for remote health and wellbeing monitoring, ISMICT, 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBor M, Vidler J, Roedig U. LoRa for the Internet of Things, EWSN, 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTechnologies OPNET. OPNET Modeler Reference Manual. Riverbed Technology; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGNS3, Documentation. GNS3 Network Emulator v2.2, 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoRa, Alliance. LoRaWAN Regional Parameters v1.0.3, 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriis HT. A note on a simple transmission formula, Proc. IRE, vol. 34, no. 5, pp. 254\u0026ndash;256, 1946.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRappaport TS. Wireless Communications: Principles and Practice. 2nd ed. Prentice Hall; 2002.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Internet of Battlefield Things, ns-3, LoRaWAN, IFF Reliability, Network Simulation, Terrain-Aware Propagation","lastPublishedDoi":"10.21203/rs.3.rs-9149480/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9149480/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper describes an ns-3 simulation of an Internet of Battlefield Things (IoBT) network built to study Identification Friend or Foe (IFF) reliability under terrain-heterogeneous LoRaWAN propagation. The simulation covers a 10 × 10 km battlespace with 200 Class A LoRaWAN end-devices, gateways on hexagonal grids at five inter-gateway separations (200–7000 m), and nine terrain classes parameterised by path-loss exponents from published LoRa measurements (n = 1.8 to 4.4). A custom event-logging module records every transmission and gateway reception at millisecond resolution, producing 3.26 × 10⁵ communication events across 45 independent runs. Key results: IFF verification collapses sharply in Forest, Hilly, and High Altitude terrain beyond 500 m gateway spacing; and ADR masking causes single-gateway PDR to remain acceptable at separations where dual-gateway verification has already failed.\u003c/p\u003e","manuscriptTitle":"ns-3 Simulation of an IoBT Network for IFF Reliability Study Under Terrain- Heterogeneous LoRaWAN Propagation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 13:41:36","doi":"10.21203/rs.3.rs-9149480/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ce637a6-bfe5-4229-b449-afb983023f71","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T09:11:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 13:41:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9149480","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9149480","identity":"rs-9149480","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}