MACO-QCR: Multi-Objective ACO-Based QoS-Aware Cross-Layer Routing Protocols in WSN

Recent advances in microelectronics have encouraged the implementation of a wireless sensor network (WSN) in Intelligent Monitoring Systems (IMSs). The event data traffic in IMS applications requires timely and reliable delivery in order to react immediately with the appropriate actions. However, a sensor node's limited energy resource forces a trade-off to be made between delay and energy consumption while determining the route towards the BS. To cope up with the multi-constrained routing problem introduced by event data traffic in IMS, a multi-objective ant-colony-optimization based QoS-aware cross-layer routing (MACO-QCR) protocol has been proposed for inter-cluster communication in WSN-based IMS. The ACO algorithm is improved to be a multi-objective routing algorithm with considering the energy consumption cost and the end-to-end delay cost of a routing path as two optimization objectives, in which a routing path is produced through the multi pheromone information and the multi heuristic information consisting of two objective functions. To obtain Pareto optimal solutions, the MACO-ACO uses an external archive method based on fuzzy membership function to assign fitness values to the non-dominated solutions. Moreover, MACO-QCR adopts a cross layer approach that enables interaction between routing and medium access layer (MAC) layers for relay node selection. The simulation analysis shows that MACO-QCR has up to 10.5% and 6.8% more energy efficient, and has up to 19.1% and 25.3% less end-to-end delay compared to IAMQER and O_ARA protocols for a varying proportion of nodes having event data traffic in the network.

Automated summary

A multi-objective ant-colony-optimization based QoS-aware cross-layer routing protocol has been proposed for Wireless Sensor Networks in Intelligent Monitoring Systems. By improving the ACO algorithm and considering energy consumption and end-to-end delay costs, this protocol achieves superior energy efficiency and lower end-to-end delay compared to traditional protocols, as demonstrated in simulation analysis.