Design and Development of Multi-Objective Hybrid Clustering Framework for Smart City in India Using Internet of Things

R. Roshan, O. Rishi
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引用次数: 1

Abstract

The drastic growth of smart city has considerably gained attention around the world in the international policies and systematic literature. Numerous specialists should include diverse opinions owing to the hurdles to the design of smart cities in India. Thus, these experts have also offered their opinions regarding public, agriculture, industry and academia-fields, which help in developing the smart cities. Generally, more limitations have to be faced with offering energy optimisation and superior performance in Internet of Things (IoT)-enabled smart cities. In wireless sensor networks (WSNs) and IoT, the sensors or IoT devices or nodes are often grouped into clusters that result in selecting the cluster head, which gathers information from the entire nodes in cluster and plainly transmits with the base station. This paper makes an attempt on the development of smart cities in India using the hybrid meta-heuristic-based multi-objective cluster head selection model. The proposed model focusses on the design and development of new smart city model applicable for India by considering a multi-objective function using the constraints like distance, delay, energy, load and temperature of the IoT devices. The optimisation of these variables during the smart city development model by IoT is accomplished by a new hybrid Deer Hunting-Tunicate Swarm Optimisation (DH-TSO) algorithm. The performance of the proposed model is verified through a comparative analysis using various state-of-the-art optimisation models by concerning the number of alive nodes, and normalised energy, and thus ensures the overall lifetime of the network.
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基于物联网的印度智慧城市多目标混合聚类框架设计与开发
智慧城市的迅猛发展在国际政策和系统文献中得到了极大的关注。由于印度智慧城市设计的障碍,许多专家应该包括不同的意见。因此,这些专家也就公共、农业、工业和学术领域提出了他们的意见,这有助于发展智慧城市。一般来说,在支持物联网(IoT)的智慧城市中,提供能源优化和卓越性能必须面临更多限制。在无线传感器网络和物联网中,通常将传感器或物联网设备或节点分组成集群,从而选择簇头,簇头从集群中的整个节点收集信息并与基站直接传输。本文利用基于元启发式的混合多目标簇头选择模型对印度智慧城市的发展进行了尝试。所提出的模型侧重于设计和开发适用于印度的新智慧城市模型,通过考虑使用物联网设备的距离,延迟,能量,负载和温度等约束的多目标函数。在物联网智能城市发展模型中,这些变量的优化是通过一种新的混合猎鹿-被囊动物群优化(DH-TSO)算法来完成的。通过使用各种最先进的优化模型进行比较分析,通过关注活动节点的数量和归一化能量,验证了所提出模型的性能,从而确保了网络的整体寿命。
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