Adaptive priority scheduling of Internet of Things data for disaster management in smart cities

In the recent context of the emergence of smart cities, the massive amount of data generated by connected objects has led to unprecedented demands in terms of data transfer. The various constraints linked to their number, their characteristics, and their transmission are even greater and dim the effectiveness, in their regard, of traditional data planning schemes. As a result, the need to minimize the delivery time of urgent packets while reducing the average data delay, the difficulty in choosing and combining the appropriate criteria for classifying and prioritizing data, and the loss of packets are of continuing concern. In this paper, we propose an adaptive scheduling model based on multilevel priority packet classification, preemptive packet queuing with dynamic and adaptive reordering, contingency migration of packets in critical situations, and adaptive criticality-based selection of packet next-hop. We introduce two new parameters for scheduling decisions: the ratio of per-level deadlines reflecting the evolution of a packet in the network and the migration coefficient based on the experience of same-characteristic packets. Performance evaluation shows that the proposed model effectively prevents data loss and prioritizes the transfer of emergency data over a hierarchical wireless sensor network. Moreover, it guarantees the shortest delays for urgent data with an improvement of 31% and promotes fairness toward less urgent ones. The lowest delivery rate observed with the proposed method is 99.9%.