为了弥补AODV(Ad Hoc on Demand Distance Vector)路由协议安全方面的缺点,同时获取基于该优化协议下的最短路径,文中采用信任机制模型方法,在原始AODV路由协议上改进TAODV(Trusted Ad Hoc on Demand Distance Vector Routing Algorithm...为了弥补AODV(Ad Hoc on Demand Distance Vector)路由协议安全方面的缺点,同时获取基于该优化协议下的最短路径,文中采用信任机制模型方法,在原始AODV路由协议上改进TAODV(Trusted Ad Hoc on Demand Distance Vector Routing Algorithm)路由协议。该协议以各个节点的信任值为基础进行相关运算,从而判断在路由协议运作过程中的路径信任值。通过在MATLAB平台中对相关参数进行设置,对改进后的TAODV协议进行模拟。仿真结果表明,改进后的TAODV路由协议在归一化路由开销、最小跳数和最优路径方面均优于传统的AODV路由协议,同时增强了网络的鲁棒性和抗毁性。展开更多
Routing is a challenging task in Wireless Sensor Networks (WSNs) due to the limitation in energy and hardware capabilities in WSN nodes. This challenge prompted researchers to develop routing protocols that satisfy WS...Routing is a challenging task in Wireless Sensor Networks (WSNs) due to the limitation in energy and hardware capabilities in WSN nodes. This challenge prompted researchers to develop routing protocols that satisfy WSNs needs. The main design objectives are reliable delivery, low energy consumption, and prolonging network lifetime. In WSNs, routing is based on local information among neighboring nodes. Routing decisions are made locally;each node will select the next hop without any clue about the other nodes on the path. Although a full knowledge about the network yields better routing, that is not feasible in WSNs due to memory limitation and to the high traffic needed to collect the needed data about all the nodes in the network. As an effort to try to overcome this disadvantage, we are proposing in this paper aware diffusion routing protocol. Aware diffusion follows a semi-holistic approach by collecting data about the available paths and uses these data to enforce healthier paths using machine learning. The data gathering is done by adding a new stage called data collection stage. In this stage, the protocol designer can determine which parameters to collect then use these parameters in enforcing the best path according to certain criteria. In our implementation of this paradigm, we are collecting total energy on the path, lowest energy level on the path, and hop count. Again, the data collected is designer and application specific. The collected data will be used to compare available paths using non-incremental learning, and the outcome will be preferring paths that meet the designer criteria. In our case, healthier and shorter paths are preferred, which will result in less power consumption, higher delivery rate, and longer network life since healthier and fewer nodes will be doing the work.展开更多
文摘为了弥补AODV(Ad Hoc on Demand Distance Vector)路由协议安全方面的缺点,同时获取基于该优化协议下的最短路径,文中采用信任机制模型方法,在原始AODV路由协议上改进TAODV(Trusted Ad Hoc on Demand Distance Vector Routing Algorithm)路由协议。该协议以各个节点的信任值为基础进行相关运算,从而判断在路由协议运作过程中的路径信任值。通过在MATLAB平台中对相关参数进行设置,对改进后的TAODV协议进行模拟。仿真结果表明,改进后的TAODV路由协议在归一化路由开销、最小跳数和最优路径方面均优于传统的AODV路由协议,同时增强了网络的鲁棒性和抗毁性。
文摘Routing is a challenging task in Wireless Sensor Networks (WSNs) due to the limitation in energy and hardware capabilities in WSN nodes. This challenge prompted researchers to develop routing protocols that satisfy WSNs needs. The main design objectives are reliable delivery, low energy consumption, and prolonging network lifetime. In WSNs, routing is based on local information among neighboring nodes. Routing decisions are made locally;each node will select the next hop without any clue about the other nodes on the path. Although a full knowledge about the network yields better routing, that is not feasible in WSNs due to memory limitation and to the high traffic needed to collect the needed data about all the nodes in the network. As an effort to try to overcome this disadvantage, we are proposing in this paper aware diffusion routing protocol. Aware diffusion follows a semi-holistic approach by collecting data about the available paths and uses these data to enforce healthier paths using machine learning. The data gathering is done by adding a new stage called data collection stage. In this stage, the protocol designer can determine which parameters to collect then use these parameters in enforcing the best path according to certain criteria. In our implementation of this paradigm, we are collecting total energy on the path, lowest energy level on the path, and hop count. Again, the data collected is designer and application specific. The collected data will be used to compare available paths using non-incremental learning, and the outcome will be preferring paths that meet the designer criteria. In our case, healthier and shorter paths are preferred, which will result in less power consumption, higher delivery rate, and longer network life since healthier and fewer nodes will be doing the work.
