Low‐Energy Secure Routing Protocol for WSNs Based on Multiobjective Ant Colony Optimization Algorithm

Abstract

As one of the three pillars of information technology, wireless sensor networks (WSNs) have been widely used in environmental detection, healthcare, military surveillance, industrial data sampling, and many other fields due to their unparalleled advantages in deployment cost, network power consumption, and versatility. The advent of the 5G standard and the era of Industry 4.0 have brought new opportunities for the development of wireless sensor networks. However, due to the limited power capacity of the sensor nodes themselves, the harsh deployment environment will bring a great difficulty to the energy replenishment of the sensor nodes, so the energy limitation problem has become a major factor limiting its further development; how to improve the energy utilization efficiency of WSNs has become an urgent problem in the scientific and industrial communities. Based on this, this paper researches the routing technology of wireless sensor networks, from the perspective of improving network security, and reducing network energy consumption, based on the study of ant colony optimization algorithm, further studies the node trust evaluation mechanism, and carries out the following research work: (1) study the energy consumption model of wireless sensor networks; (2) basic ant colony algorithm improvement; (3) multiobjective ant colony algorithm based on wireless sensor routing algorithm optimization. In this study, the NS2 network simulator is used as a simulation tool to verify the performance of the research algorithm. Compared with existing routing algorithms, the simulation results show that the multiobjective ant colony optimization algorithm has better performance in evaluation indexes such as life cycle, node energy consumption, node survival time, and stability compared with the traditional algorithm and the dual cluster head ant colony optimization algorithm.

Secure Adaptive Cluster based Routing Using Multi Objective-Trust based Hybrid Optimization Algorithm for CPS

“…The performance of the MO-THOA is analyzed using PDR, AEED and NRO. Here, existing methods namely GU-WOA [18], MOACO [19] and MCRP [20] are used to analyze the efficiency of the MO-THOA method. These existing methods are simulated by using the same specifications mentioned in Table 1.…”

“…Wang [19] developed the MOACO for developing the node-trusted secure routing over the network. Here, the security of the nodes was evaluated based on the trust valuation model.…”

“…( 12) which also defines how much packet drop rate is reduced by the MO-THOA. The PDR analysis of MO-THOA, GU-WOA [18], MOACO [19] and MCRP [20] for varying nodes is shown in Fig. 2.…”

“…Additionally, the node failure or link failure or monitored using residual energy which further enhances the PDR. However, the inappropriate objective function of MOACO [19] causes less PDR while broadcasting the packets whereas MCRP [20] failed to mitigate the malicious nodes during the data broadcasting process.…”

“…𝐴𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 (14) The NRO analysis of MO-THOA, GU-WOA [18], MOACO [19] and MCRP [20] for varying nodes is shown in Fig. 4.…”