A Study of Energy-Efficient Routing Protocols for Industrial IoT WSNs Combining GA and ACO Algorithms

Obstacles such as buildings and equipment in industrial scenarios can cause multipath propagation of wireless signals, resulting in changes in signal strength and phase, and multipath fading. This requires that the routing protocol can dynamically adapt to the signal changes. To adapt to data transmission path selection in industrial IoT WSNs, an energy-efficient routing protocol is devised, leveraging a hybrid approach that combines genetic algorithm (GA) and ant colony algorithm (ACO). Based on the industrial IoT WSN routing protocol architecture, the method calculates the coverage rate and communication energy consumption of industrial IoT WSN nodes. Four criteria are considered for selecting cluster head nodes: node coverage rate, energy level, location, and number of neighboring nodes. Genetic algorithms are employed to select these cluster head nodes, and subsequently, ACO algorithms determine the optimal data transmission paths based on these selections. Based on the result of cluster head node selection, the best data transmission path is determined by ant colony algorithm. The test results show that: the method can obtain cluster head nodes that better meet the transmission requirements of the network structure based on the selection conditions of cluster head nodes; it can adapt to the selection of data transmission paths for industrial IoT WSNs; the connectivity of the network is more than 88.5%; and the communication load of the base station is less than 5% of the standard.