物联网(IoT)是一种用于感测和监视环境以减少灾难影响的有前途的技术。能源是物联网设备的主要关注点之一,因为物联网设备中使用的传感器是电池供电的。因此,重要的是减少能源消耗,特别是在容易发生灾害的情况下进行数据传输。基于集群的通信有助于减少数据传输过程中节点的能量衰减,并延长网络寿命。已经提出了许多混合组合算法用于聚类和路由协议,以提高灾难场景中的网络寿命。然而,这些协议的性能根据底层网络配置和所考虑的优化参数而变化很大。在这项研究中,我们使用了与灾难场景最相关的聚类参数,例如节点的剩余能量,距离下沉,和网络覆盖。然后,我们提出了生物启发的混合BOA-PSO算法,其中蝴蝶优化算法(BOA)用于聚类,粒子群优化(PSO)用于路由协议。将所提出的算法的性能与各种基准协议的性能进行了比较:LEACH,DEEC,PSO,PSO-GA,和PSO-HAS。剩余能量,网络吞吐量,和网络寿命被认为是性能指标。仿真结果表明,该算法有效地节约了剩余能量,在短程场景中实现17%以上的改进,在远程场景中实现10%以上的改进。在吞吐量方面,与LEACH相比,所提出的方法提高了60%的性能,与DEEC相比提高了53%,与PSO相比提高了37%。此外,所提出的方法与LEACH和DEEC相比,数据包丢弃减少了60%,与PSO相比减少了30%。与基准算法相比,它将网络寿命提高了10-20%。
The Internet of Things (
IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for
IoT devices, as sensors used in
IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node\'s energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node\'s residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10-20% compared to the benchmark algorithms.