Efficient Load Balancing for Heterogeneous Radio-Replication-Combined LoRaWAN

LoRa Wide Area Network (LoRaWAN), an emerging IoT protocol, has been popularized in large-scale applications given its long-range and low-power properties. Hitherto, an appropriate traffic model is lacked in LoRaWAN to estimate the heterogeneous arriving traffic at network server cluster (NSC) in LoRaWAN, rendering inefficient computation power planning or even processing failure. The radio replication commonly existed in the arriving traffic at NSC in LoRaWAN further results in difficulty on estimating makespan. To overcome the aforementioned limitations, a heterogeneous traffic aggregation model (HTAM) considering radio replications is proposed to estimate the arriving traffic for LoRaWAN. In addition, a radio-replication-combined supermarket model (RRC-SM), on top of HTAM, is proposed to achieve load balancing among servers in LoRaWAN. Besides, non-dominated sorting genetic algorithm (NSGA) based multi-objective optimization is developed to simultaneously minimize the cost and latency on NSC. Experiments reveal that the proposed HTAM and RRC-SM agree well with the simulation outcome. Under the arriving traffic estimated as 6.16 erlangs with 4 radio replications of each arriving packet on average, the proposed RRC-SM provides more than 50% reduction on the total processing latency and 75% reduction on the number of servers in NSC than other existing models.