Long Range Wide Area Networks (LoRaWAN) offer easy deployment, robustness against interference, and operational longevity to energy constrained IoTdevices which communicate in a best-e_ort fashion in extended ranges. However, the simple (ALOHA-like) design of the MAC layer leads to packet collisions in dense LoRaWAN deployments with high traffic loads. To achieve scalability above a few hundreds of devices, time division is not an option, since LoRaWAN is asynchronous regarding communication. Further, feedback mechanisms are discouraged due to duty cycle limitations. In this document, we propose Spreading Factor MAC (SFMAC); a distributed and energy efficient MAC protocol for LoRaWAN, wherein {for the _rst time to the best of our knowledge{ high-SF channels are dedicated strictly to Channel Sensing (CS), while low-SF channels are focused on data-transmission. The Capture E_ect (CE) phenomena that is manifested in the PHY layer is extensively evaluated on-_eld and embodied in the SFMAC operating principle. The dedicated high-SF sensing allows e_ective revealing of hidden devices' transmissions without a_ecting the low-SF tra_c. We showcase the impact of SFMAC in scalability by designing a realistic implementation of the mechanism in ns-3. We report a x2.08 improvement in channel utilization and x2 goodput compared to LoRaWAN, without substantially increasing complexity.

Locating people inside buildings is still an unsolved problem. There is a lot
of research going on in this field and many different solutions using different
techniques have been proposed. However, there is no widely accepted indoor
localization solution like how GPS is for outdoor localization due to less accuracy, higher hardware requirement, cost etc,. We introduce a system that locates
people indoors more accurately.