A Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneous wireless sensor networks, has been done. Through simulations of random deployments of nodes over a square area using different densities, ...A Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneous wireless sensor networks, has been done. Through simulations of random deployments of nodes over a square area using different densities, assuming that our network is composed by Anchor nodes （special sensors with known position） and simple Sensor nodes, the latter are supposed to estimate their own position after being placed within the coverage area with the minimum Anchor nodes needed to ＇feed＇ them with the necessary information. The goal is then to assist decision-makers in selecting among different alternatives to deploy the networks, according to resources features and availability, hence this method provides an estimate value of how many Anchor nodes should be deployed in a given area to trigger the location algorithm in the greatest possible number of Sensor nodes in the network.展开更多
优化链路状态路由(Optimized Link State Routing,OLSR)协议采用多点中继(Multi Point Relays,MPR)机制以减轻网络负载。然而,当传统MPR算法的最大覆盖度原则出现遗漏时,所选出的MPR集合并非最佳,无法达到最大程度的资源优化。为提升协...优化链路状态路由(Optimized Link State Routing,OLSR)协议采用多点中继(Multi Point Relays,MPR)机制以减轻网络负载。然而,当传统MPR算法的最大覆盖度原则出现遗漏时,所选出的MPR集合并非最佳,无法达到最大程度的资源优化。为提升协议在大规模网络中的表现,提出了两种改进方案来优化传统MPR算法:一种是基于果蝇思想的MPR方案(FruitFly MPR,FF-MPR),另一种是基于逆向贪心策略的MPR方案(Backward Greed MPR,BG-MPR)。Matlab仿真结果显示,这两种方案都有效规避了传统MPR算法的冗余问题。但是,FF-MPR因其固有的随机性和较长的计算时间而不适合节点多、移动性强的网络环境;BG-MPR在特殊情况下可能会产生新的冗余问题。为此,对BG-MPR中新产生的冗余进行了深入研究,提出了相应的解决措施,开发出基于改进逆向贪心策略的OLSR协议(Reverse Greed OLSR,RG-OLSR),使用OPNET仿真平台对其进行了测试。仿真结果证明,RG-OLSR在控制消息开销、端到端时延和时延抖动等关键性能指标上均优于传统的OLSR协议。展开更多
文摘A Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneous wireless sensor networks, has been done. Through simulations of random deployments of nodes over a square area using different densities, assuming that our network is composed by Anchor nodes （special sensors with known position） and simple Sensor nodes, the latter are supposed to estimate their own position after being placed within the coverage area with the minimum Anchor nodes needed to ＇feed＇ them with the necessary information. The goal is then to assist decision-makers in selecting among different alternatives to deploy the networks, according to resources features and availability, hence this method provides an estimate value of how many Anchor nodes should be deployed in a given area to trigger the location algorithm in the greatest possible number of Sensor nodes in the network.
文摘优化链路状态路由(Optimized Link State Routing,OLSR)协议采用多点中继(Multi Point Relays,MPR)机制以减轻网络负载。然而,当传统MPR算法的最大覆盖度原则出现遗漏时,所选出的MPR集合并非最佳,无法达到最大程度的资源优化。为提升协议在大规模网络中的表现,提出了两种改进方案来优化传统MPR算法:一种是基于果蝇思想的MPR方案(FruitFly MPR,FF-MPR),另一种是基于逆向贪心策略的MPR方案(Backward Greed MPR,BG-MPR)。Matlab仿真结果显示,这两种方案都有效规避了传统MPR算法的冗余问题。但是,FF-MPR因其固有的随机性和较长的计算时间而不适合节点多、移动性强的网络环境;BG-MPR在特殊情况下可能会产生新的冗余问题。为此,对BG-MPR中新产生的冗余进行了深入研究,提出了相应的解决措施,开发出基于改进逆向贪心策略的OLSR协议(Reverse Greed OLSR,RG-OLSR),使用OPNET仿真平台对其进行了测试。仿真结果证明,RG-OLSR在控制消息开销、端到端时延和时延抖动等关键性能指标上均优于传统的OLSR协议。
