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An approach based on fuzzy inference system and ant colony optimization for improving the performance of routing protocols in Wireless Sensor Networks

机译：基于模糊推理系统和蚁群优化的无线传感器网络路由协议性能改进方法

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The Wireless Sensor Networks (WSNs) are composed of small sensor nodes capable of sensing (collecting), processing and transmitting data related to some phenomenon in the environment. However, the sensor nodes have severe constraints, such as: low network bandwidth, short wireless communication range, and limited CPU processing capacity, memory storage and power supply. Therefore, maximizing the benefits of limited resources in WSNs have become one relevant and challenging issue. One of the most relevant problem is related with the energy consumption during data transmission, since, sensor nodes are battery-powered and recharging or replacing batteries, in most cases, is infeasible. Communication in WSN consumes more energy than sensing and processing performed by the network nodes. The strategy proposed in this paper, to reduce the energy consumption, consists in optimizing the operations of routing protocols. The WSN routing protocols must have self configuration features in order to find out which is the best route for communication, thus increasing delivery assurance and decreasing the energy consumption between nodes that comprise the network. This paper presents a proposal for estimating the quality of routes using fuzzy systems to assist the Directed Diffusion routing protocol. The fuzzy system is used to estimate the degree of the route quality, based on the number of hops and the energy level of the nodes that compose a route. An Ant Colony Optimization (ACO) algorithm is used to adjust, in an automatic way, the rule base of the fuzzy system in order to improve the classification strategy of routes, hence increasing the energy efficiency of the network. The simulations showed that the proposal is effective from the point of view of three metrics: packet loss rate, message delay to the sink node and time of death of the first sensor node.
机译：无线传感器网络（WSN）由能够感应（收集），处理和传输与环境中某些现象有关的数据的小型传感器节点组成。但是，传感器节点具有严格的约束，例如：低网络带宽，较短的无线通信范围以及有限的CPU处理能力，内存存储和电源。因此，最大化WSN中有限资源的收益已成为一个相关且具有挑战性的问题。最相关的问题之一与数据传输期间的能耗有关，因为传感器节点由电池供电，并且在大多数情况下无法为电池充电或更换电池。 WSN中的通信比网络节点执行的感测和处理消耗更多的能量。本文提出的减少能耗的策略在于优化路由协议的操作。 WSN路由协议必须具有自我配置功能，以便找出哪个是最佳的通信路径，从而提高了传递保证，并减少了组成网络的节点之间的能耗。本文提出了一种使用模糊系统来估计有向扩散路由协议的路由质量的建议。模糊系统用于根据跳数和组成路由的节点的能量级别来估计路由质量。为了改进路线的分类策略，使用蚁群优化算法自动调整模糊系统的规则库，从而提高了网络的能效。仿真表明，从三个指标的角度来看，该建议是有效的：丢包率，到宿节点的消息延迟和第一个传感器节点的死亡时间。

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