- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Wireless sensor networks (WSNs) are widely applied in smart manufacturing because their installation does not need fixed infrastructure and can be used where cabling and power supply are difficult. Given the limited energy supply and computing capability of a WSN, an efficient routing algorithm for data transmission is essential for its performance. Ant colony optimization is used in WSNs to identify shortest paths, and thus reduce the energy consumption of the network. However, ant colony optimization is prone to falling into local optima and convergences slowly. We hence propose an improved ant colony algorithm that can be used to construct the sensor node transfer function and pheromone update rule, and adaptively choose a data route by adopting the advantages of the dynamic state of the network. The simulation results show that the proposed method can further reduce energy consumption, time delay, and data packet losses. Thus, the quality of service of the WSN is improved by its use.
This paper proposed the IACO routing algorithm, which is an extended ACO. The algorithm establishes and updates the pheromone concentration of the gradient field and path by broadcasting messages and exchanging hops between neighbor nodes in the whole network. Next, data transmission, delay packet loss rate, and node residual energy are determined by calculating the corresponding probability of the neighbor nodes. Routing is selected by considering both the shortest path and highest residual energy. In the routing maintenance process, data are transferred to increase the pheromone concentration. Therefore, the energy consumption of the whole network is closer to the average while data packets are sent along the shortest paths. Consequently, the lifetime of network is effectively prolonged. Moreover, the control of special regions cannot be lost because of the rapid death of nodes along the shortest path.
According to the requirements of applications using the IACO, the QoS is divided into three levels according to the data transmission delay, data transmission reliability, and the network energy consumption. Then, IACO is used to design the wireless sensor routing algorithm, and the optimal path are selected according to the QoS level. A simulation experiment was used to test the performance of the algorithm. The simulation results show that the IACO algorithm not only reduces the data transmission delay and improves the data forwarding efficiency, but also reduces the data packet loss rate, reduces the energy consumption of the network, and prolongs the overall network performance and resource utilization.