Abstract
High‐density wireless sensor networks (HDWSNs) are usually deployed randomly, and each node of the network collects data from complex environments. Because the energy of sensor nodes is powered by batteries, it is basically impossible to replace batteries or charge in the complex surroundings. In this paper, a QoS routing energy consumption model is designed, and an improved adaptive elite ant colony optimization (AEACO) is proposed to reduce HDWSN routing energy consumption. This algorithm uses the adaptive operator and the elite operator to accelerate the convergence speed. So, as to validate the efficiency of AEACO, the AEACO is contrast with particle swarm optimization (PSO) and genetic algorithm (GA). The simulation outcomes show that the convergence speed of AEACO is sooner than PSO and GA. Moreover, the energy consumption of HDWSNs using AEACO is reduced by 30.7% compared with GA and 22.5% compared with PSO. Therefore, AEACO can successfully decrease energy consumption of the whole HDWSNs.
Highlights
Nowadays, emerging high-density wireless sensor network (HDWSNs) technologies have attracted a large number of scholars to study new QoS routing optimization algorithms in this field
The energy consumption of routing optimized by adaptive elite ant colony optimization (AEACO) is reduced by 22.5% and 30.7%, respectively, compared with particle swarm optimization (PSO) and genetic algorithm (GA) under the same experimental conditions
The results show that EIACO is more effective than PSO and GA, and its performance is always better than PSO and GA
Summary
Nowadays, emerging high-density wireless sensor network (HDWSNs) technologies have attracted a large number of scholars to study new QoS routing optimization algorithms in this field. The consequence shows that the adaptive and elite strategies proposed in this paper improve the global search capability of ant colony optimization. (1) First, we propose an improved adaptive elite ant colony optimization (AEACO), which can effectively minimize the routing energy consumption in HDWSNs. After several iterations, the energy consumption of routing optimized by AEACO is reduced by 22.5% and 30.7%, respectively, compared with PSO and GA under the same experimental conditions. Compared with the other two algorithms, the fitness after AEACO optimization converges to a small value after iteration (3) the total routing energy consumption of HDWSNs depends on the transmission and reception energy consumption of all nodes. With the increase in the number of sensors in HDWSNs, the demand for data transmission increases, and the effect of AEACO in optimizing routing energy consumption increases .
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