LoRa　technology　contributes　to　green　energy　by　enabling　efficient,　long-range　communication　for　the　Internet　of　Things　(IoT).　This　paper　addresses　the　challenges　related　to　coverage　range　in　outdoor　monitoring　systems　utilizing　LoRa,　where　the　network　performance　is　affected　by　the　density　of　gateways　(GWs)　and　end　devices　(EDs),　as　well　as　environmental　conditions.　To　mitigate　interference,　data　throughput　losses,　and　high-power　consumption,　the　proposed　spreading　factor　(SF)　and　hybrid　(data　rate|SF)　models　dynamically　adjust　the　transmission　parameters.　The　orchestration　of　concurrent　data　modifications　within　the　network　server　(NS)　is　crucial　for　uninterrupted　communication　between　GWs　and　EDs,　especially　in　monitoring　electric　vehicle　(EV)　stations　to　reduce　traffic　congestion　and　pollution.　Employing　K-means　and　density-based　spatial　clustering　of　applications　with　noise　(DBSCAN)　algorithms　optimizes　ED　allocation,　averts　data　congestion,　and　improves　the　signal-to-interference　noise　ratio　(SINR).　These　methods　ensure　seamless　information　reception　by　meticulously　allocated　EDs　across　various　GW　combinations.　To　estimate　the　free-space　losses　(FSL),　a　log-distance　path　loss　model　(log-PL)　is　used.　Exploring　various　bandwidths　(BWs),　bidirectional　communications,　and　duty　cycles　(DCs)　helps　to　prevent　saturation,　thus　prolonging　the　operational　lifespan　of　EDs.　Empirical　findings　reveal　a　notable　packet　rejection　rate　(PRR)　of　0%　for　the　DBSCAN　(hybrid　model).　In　contrast,　the　K-means　exhibits　a　PRR　ranging　from　5%　(hybrid　model)　to　35.29%　(SF　model)　for　the　ten　GWs　combination.　Notably,　the　network　saturation　is　reduced　to　10.185%　and　9.503%,　respectively,　highlighting　an　improvement　in　the　average　efficiency　of　slotted　ALOHA　(91.1%)　and　pure　ALOHA　(90.7%).　These　enhancements　increase　the　lifespan　of　EDs　to　15,465.27　days.　©　2024