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Dual Sink-Based Optimized Sensing for Intelligent Transportation Systems

机译：基于双层水槽的智能交通系统优化感应

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摘要

Wireless sensor networks (WSNs) as one of the non-negligible components of the Internet of Things (IoT) have proven to be a pillar of the Intelligent Transportation Systems (ITS). The tasks of collecting, processing and fusing the information related to traffic, accidents, congestion and also the detection of pavement distress on roads, are efficiently handled and monitored by WSN-based IoT. However, the energy constraints of the sensor nodes deployed along the roadside, create a perturbing concern for their realization in architecture. Therefore, to address this concern, in this paper, we have proposed an optimized sensing technique that employs two sinks. We term it as Dual sink-based Optimized Clustering Architecture employing Tunicate Swarm Algorithm (TSA), i.e., DOCAT in short. The fitness function of DOCAT integrates the novel fitness parameters for Cluster Head (CH) selection. The parameters are: 1) Residual and initial Energy, 2) Distance of the node from sink, 3) Intra-Cluster Average Distance (ICAD), 4) Network's average energy, and 5) Energy threshold. DOCAT is anticipated to be employed for accident prone roads, from where the critical accidental information is transmitted to healthcare venues through the IoT platform. The simulation results reveal that DOCAT acquires the proliferated performance compared to several similar algorithms in terms of the network reliability, network lifetime, throughput, and energy efficiency.
机译：无线传感器网络（WSN）作为事物互联网（物联网）的不可忽略的组件之一，已被证明是智能交通系统（其）的支柱。通过基于WSN的IOT有效地处理和监控收集，处理和融合与交通，事故，拥塞以及路面遇险检测有关的信息的任务。然而，沿着路边部署的传感器节点的能量限制为其在架构中的实现产生了扰动问题。因此，为了解决这一问题，在本文中，我们提出了一种优化的传感技术，采用了两个水槽。我们将其作为基于双层水槽的优化聚类架构，采用统一的群算法（TSA），即Docat简而言之。 Docat的健身功能集成了簇头（CH）选择的新型健身参数。参数是：1）剩余和初始能量，2）从下沉节点的距离，3）簇内平均距离（ICAD），4）网络的平均能量和5）能量阈值。预计DOCAT将用于易于道路的事故，从批判意外信息通过物联网平台传输到医疗保健场所。仿真结果表明，与网络可靠性，网络寿命，吞吐量和能效的若干相似算法相比，Docat获取了增殖性能。

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