The range of applications of Wireless Sensor Networks (WSNs) is increasing continuously despite of their serious constraints of the sensor nodes’ resources such as storage, processing capacity, communication range and energy. The main issues in WSN are the energy consumption and the delay in relaying data to the Sink node. This becomes extremely important when deploying a big number of nodes, like the case of industry pollution monitoring. We propose an artificial neural network based energy-efficient and robust routing scheme for WSNs called ELDC. In this technique, the network is trained on huge data set containing almost all scenarios to make the network more reliable and adaptive to the environment. Additionally, it uses group based methodology to increase the life-span of the overall network, where groups may have different sizes. An artificial neural network provides an efficient threshold values for the selection of a group's CN and a cluster head based on back propagation technique and allows intelligent, efficient, and robust group organization. Thus, our proposed technique is highly energy-efficient capable to increase sensor nodes’ lifetime. Simulation results show that it outperforms LEACH protocol by 42 percent, and other state-of-the-art protocols by more than 30 percent.

中文翻译：

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ELDC：基于人工神经网络的 WSN 污染监测节能和稳健路由方案

尽管无线传感器网络（WSN）的应用范围受到传感器节点资源（如存储、处理能力、通信范围和能量）的严重限制，但其应用范围仍在不断扩大。WSN 中的主要问题是能量消耗和将数据中继到 Sink 节点的延迟。这在部署大量节点时变得非常重要，例如工业污染监控的情况。我们为 WSN 提出了一种基于人工神经网络的节能且稳健的路由方案，称为 ELDC。在这种技术中，网络在包含几乎所有场景的庞大数据集上进行训练，使网络更加可靠和适应环境。此外，它使用基于组的方法来增加整个网络的寿命，其中组的大小可能不同。人工神经网络基于反向传播技术为选择组的 CN 和簇头提供了有效的阈值，并允许智能、高效和健壮的组组织。因此，我们提出的技术具有高能效，能够增加传感器节点的寿命。仿真结果表明，它的性能比 LEACH 协议高 42%，比其他最先进的协议高 30% 以上。