首页> 外文会议>International Conference on Reliability, Infocom Technologies and Optimization >Artificial Intelligence Based Energy Efficient Grid PEGASIS Routing Protocol in WSN
【24h】

Artificial Intelligence Based Energy Efficient Grid PEGASIS Routing Protocol in WSN

机译:WSN中基于人工智能的节能网格PEGASIS路由协议

获取原文

摘要

In Wireless Sensor Networks routing data and information from one node to another and to base station is a major challenges. It shows the commutation between efficiency and responsiveness. Fuzzy logic is one such protocol existing in this category which is used to reduce the overburden cluster head (CH) in Wireless sensor network (WSN), it is a rule based architecture system and with each variation in the network, rules are added in the network. For a network like WSN or MANET, the fuzzy logic may contain hundreds of rules, which makes the architecture more complicated. In this work, a hybrid Power Efficient Gathering in Sensor Information System (PEGASIS) hierarchical protocol is proposed which uses Firefly optimization technique and artificial neural network for making the lifespan of the Wireless Sensor Network (WSN) enhanced. The Firefly optimization algorithm will work on the grid deployment model. PEGASIS is best used for routing with other protocols based on sensors lifetime, total energy consumed and shows comparative results with other mainline protocols. In this work the technique used for optimization is Firefly algorithm in firefly algorithm, attractiveness and light intensity are the significant variables used to attract other firefly with more light intensity than them-self. The light intensity is directly dependent on attractiveness of the firefly. PEGASS is considered as one of the best redirecting method which allows better routing technique. In PEGASIS the sensor node location is random and every node has the capability to detect data, blend data, and equally sends the load among the nodes. Chain of nodes is made according to the positioning of the node and the nodes are plotted by using greedy algorithm. Artificial neural network (ANN) is used as a classifier to remove distortion from the network or to overcome the battery discharge problem. The accomplishment of the proposed work can be assessed by measuring the performance parameters named as Total number of packet transmitted, Energy dissipation, Throughput, Network lifetime by using MATLAB simulator.
机译:在无线传感器网络中,将数据和信息从一个节点路由到另一个节点再到基站是一项重大挑战。它显示了效率和响应能力之间的转换。模糊逻辑是此类中存在的一种这样的协议,用于减少无线传感器网络(WSN)中的过载群集头(CH),它是基于规则的体系结构系统,网络中的每个变化都会在规则中添加规则。网络。对于像WSN或MANET这样的网络,模糊逻辑可能包含数百个规则,这使体系结构更加复杂。在这项工作中,提出了一种混合信息高效收集传感器信息系统(PEGASIS)分层协议,该协议使用Firefly优化技术和人工神经网络来提高无线传感器网络(WSN)的寿命。 Firefly优化算法将适用于网格部署模型。 PEGASIS最适合用于基于传感器寿命,总能耗的其他协议进行路由,并显示与其他主线协议的比较结果。在这项工作中,用于优化的技术是萤火虫算法中的萤火虫算法,吸引力和光强度是用来吸引比自己强光强度的其他萤火虫的重要变量。光强度直接取决于萤火虫的吸引力。 PEGASS被认为是最佳的重定向方法之一,它可以提供更好的路由技术。在PEGASIS中,传感器节点的位置是随机的,每个节点都具有检测数据,混合数据并平均在节点之间发送负载的能力。根据节点的位置制作节点链,并使用贪心算法绘制节点图。人工神经网络(ANN)用作分类器,以消除网络中的失真或克服电池放电问题。可以通过使用MATLAB模拟器测量性能参数(传输的数据包总数,能耗,吞吐量,网络寿命)来评估所建议工作的完成情况。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号