LoRaWAN is one of the leading Low Power WideArea Network (LPWAN) architectures. It was originally designedfor systems consisting of static sensor or Internet of Things (IoT)devices and static gateways. It was recently updated to introducenew features such as nano-second timestamps which open upapplications to enable LoRaWAN to be adopted for mobile devicetracking and localisation. In such mobile scenarios, devices couldtemporarily lose communication with the gateways because ofinterference from obstacles or deep fading, causing throughputreduction and delays in data transmission. To overcome thisproblem, we propose a new data forwarding scheme. Instead ofholding the data until the next contact with gateways, devices canforward their data to nearby devices that have a higher probabil-ity of being in contact with gateways. We propose a new networkmetric called Real-Time Contact-Aware Expected TransmissionCount (RCA-ETX) to model this contact probability in real-time. Without making any assumption on mobility models, thismetric exploits data transmission delays to model complex devicemobility. We also extend RCA-ETX with a throughput-optimalstochastic backpressure routing scheme and propose Real-TimeOpportunistic Backpressure Collection (ROBC), a protocol tocounter the stochastic behaviours resulting from the dynamicsassociated with mobility. To apply our approaches seamlesslyto LoRaWAN-enabled devices, we further propose two newLaRaWAN classes, namely Modified Class-C and Queue-basedClass-A. Both of them are compatible with LoRaWAN Class-Adevices. Our data-driven experiments, based on the London busnetwork, show that our approaches can reduce data transmissiondelays up to25%and provide a53%throughput improvementin data transfer performance.