School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.
Sensors (Basel). 2022 May 21;22(10):3910. doi: 10.3390/s22103910.
Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algorithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter- and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters popsize=60, number of rooster Nr=0.3, number of hen’s Nh=0.6 and swarm updating frequency θ=10. Further, comparative results proved that HIOA is more effective than traditional bio-inspired algorithms in terms of node death percentage, average residual energy and network lifetime by 12%, 19% and 23%.
聚类是优化传感器支持的物联网 (IoT) 网络能量消耗的一种很有前途的技术。在网络中不均匀地分布簇头 (CH)，反复选择相同的 IoT 节点作为 CH，并在其他 CH 的通信范围内识别 CH 是导致 IoT 网络能量消耗更高的主要问题。在本文中，使用模糊逻辑、仿生鸡群优化 (CSO) 和遗传算法，提出了一种混合智能优化算法 (HIOA)，以最小化 IoT 网络中的总能量消耗。在 HIOA 中，形成 IoT 节点簇的关键思想取决于找到具有最小适应度函数值的染色体，该适应度函数与相关网络参数有关。适应度函数包括最小化簇间和簇内距离，以减少接口和每轮通信的最小能量消耗。CSO 的分层顺序分类利用遗传方法的交叉和变异操作来增加种群多样性，最终解决 CH 分布不均和平衡网络负载的问题。在 MATLAB2019A 上对所提出的 HIOA 算法进行了仿真，并对其与 CSO 参数的性能进行了分析，结果发现，通过设置参数 popsize=60、Nr=0.3、Nh=0.6 和 swarm 更新频率θ=10，可获得所提出的 HIOA 算法的最佳适应度值。此外，对比结果证明，HIOA 在节点死亡率、平均剩余能量和网络寿命方面比传统的仿生算法更有效，分别提高了 12%、19%和 23%。
