In the paper, we consider a network of energy constrained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmitting sensed data to a base station （via clusterhead...In the paper, we consider a network of energy constrained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmitting sensed data to a base station （via clusterhead） for further processing. The key problem focuses on how to reduce the power consumption of wireless microsensor networks. The core includes the energy efficiency of clusterheads and that of cluster members. We first extend low-energy adaptive clustering hierarchy （LEACH）＇s stochastic clusterhead selection algorithm by a factor with distance-based deterministic component （LEACH-D） to reduce energy consumption for energy efficiency of clusterhead. And the cost function is proposed so that it balances the energy consumption of nodes for energy efficiency of cluster member. Simulation results show that our modified scheme can extend the network life around up to 40% before first node dies. Through both theoretical analysis and numerical results, it is shown that the proposed algorithm achieves better performance than the existing representative methods.展开更多
With the spectacular progress of technology, we have witnessed the appearance of wireless sensor networks (WSNs) in several fields. In a hospital for example, each patient will be provided with one or more wireless se...With the spectacular progress of technology, we have witnessed the appearance of wireless sensor networks (WSNs) in several fields. In a hospital for example, each patient will be provided with one or more wireless sensors that gather his physiological data and send them towards a base station to treat them on behalf of the clinicians. The WSNs can be integrated on a building surface to supervise the state of the structure at the time of a destroying event such as an earthquake or an explosion. In this paper, we presented a Mobility-Energy-Degree-Distance to the Base Station (MED-BS) Clustering Algorithm for the small-scale wireless Sensor Networks. A node with lower mobility, higher residual energy, higher degree and closer to the base station is more likely elected as a clusterhead. The members of each cluster communicate directly with their ClusterHeads (CHs) and each ClusterHead aggregates the received messages and transmits them directly to the base station. The principal goal of our algorithm is to reduce the energy consumption and to balance the energy load among all nodes. In order to ensure the reliability of MED-BS, we compared it with the LEACH (Low Energy Adaptive Clustering Hierarchy) clustering algorithm. Simulation results prove that MED-BS improves the energy consumption efficiency and constructs a stable structure which can support new sensors without returning to the clusters reconstruction phase.展开更多
移动自组织网络(Mobile Ad Hoc Networks)具有动态变化的拓扑结构、无中心和自组织等特点,如何对它进行有效的管理,至今还没有得到很好的解决,经过查阅大量有关分簇算法的资料,该文提出了一种新型分簇算法NAOW(a New Adaptive On-demand...移动自组织网络(Mobile Ad Hoc Networks)具有动态变化的拓扑结构、无中心和自组织等特点,如何对它进行有效的管理,至今还没有得到很好的解决,经过查阅大量有关分簇算法的资料,该文提出了一种新型分簇算法NAOW(a New Adaptive On-demand Weighting)。该算法在AOW(Adaptive On-demand Weighting)算法的基础上提出了一些改进,从而提高了网络管理的灵活性和可扩展性,使之更适合于管理大规模、多环境的Ad Hoc无线网络。展开更多
针对水声传感器网络分簇协议中簇头数量自由度高以及分布不均所导致能量消耗过多的缺陷,提出一种基于优化分簇的、能耗均匀的分布式LEACH(low energy adaptive clustering hierarchy)协议。改进分布式簇头选择机制,每轮中簇头选举由一...针对水声传感器网络分簇协议中簇头数量自由度高以及分布不均所导致能量消耗过多的缺陷,提出一种基于优化分簇的、能耗均匀的分布式LEACH(low energy adaptive clustering hierarchy)协议。改进分布式簇头选择机制,每轮中簇头选举由一次选举改为多次选举,引入最优成簇规模控制策略,实现簇头节点的位置分布优化,提高簇头数目稳定性,实现均衡网络能量。仿真结果表明,该改进LEACH协议能解决水声传感器网络分簇协议存在的能量问题,使网络的能量消耗更加均匀,并在一定程度上延长网络的生存期限。展开更多
基金the Science and Technology Research Project of Chongqing Municipal Education Commission of China (080526)
文摘In the paper, we consider a network of energy constrained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmitting sensed data to a base station （via clusterhead） for further processing. The key problem focuses on how to reduce the power consumption of wireless microsensor networks. The core includes the energy efficiency of clusterheads and that of cluster members. We first extend low-energy adaptive clustering hierarchy （LEACH）＇s stochastic clusterhead selection algorithm by a factor with distance-based deterministic component （LEACH-D） to reduce energy consumption for energy efficiency of clusterhead. And the cost function is proposed so that it balances the energy consumption of nodes for energy efficiency of cluster member. Simulation results show that our modified scheme can extend the network life around up to 40% before first node dies. Through both theoretical analysis and numerical results, it is shown that the proposed algorithm achieves better performance than the existing representative methods.
文摘With the spectacular progress of technology, we have witnessed the appearance of wireless sensor networks (WSNs) in several fields. In a hospital for example, each patient will be provided with one or more wireless sensors that gather his physiological data and send them towards a base station to treat them on behalf of the clinicians. The WSNs can be integrated on a building surface to supervise the state of the structure at the time of a destroying event such as an earthquake or an explosion. In this paper, we presented a Mobility-Energy-Degree-Distance to the Base Station (MED-BS) Clustering Algorithm for the small-scale wireless Sensor Networks. A node with lower mobility, higher residual energy, higher degree and closer to the base station is more likely elected as a clusterhead. The members of each cluster communicate directly with their ClusterHeads (CHs) and each ClusterHead aggregates the received messages and transmits them directly to the base station. The principal goal of our algorithm is to reduce the energy consumption and to balance the energy load among all nodes. In order to ensure the reliability of MED-BS, we compared it with the LEACH (Low Energy Adaptive Clustering Hierarchy) clustering algorithm. Simulation results prove that MED-BS improves the energy consumption efficiency and constructs a stable structure which can support new sensors without returning to the clusters reconstruction phase.
文摘移动自组织网络(Mobile Ad Hoc Networks)具有动态变化的拓扑结构、无中心和自组织等特点,如何对它进行有效的管理,至今还没有得到很好的解决,经过查阅大量有关分簇算法的资料,该文提出了一种新型分簇算法NAOW(a New Adaptive On-demand Weighting)。该算法在AOW(Adaptive On-demand Weighting)算法的基础上提出了一些改进,从而提高了网络管理的灵活性和可扩展性,使之更适合于管理大规模、多环境的Ad Hoc无线网络。
文摘针对水声传感器网络分簇协议中簇头数量自由度高以及分布不均所导致能量消耗过多的缺陷,提出一种基于优化分簇的、能耗均匀的分布式LEACH(low energy adaptive clustering hierarchy)协议。改进分布式簇头选择机制,每轮中簇头选举由一次选举改为多次选举,引入最优成簇规模控制策略,实现簇头节点的位置分布优化,提高簇头数目稳定性,实现均衡网络能量。仿真结果表明,该改进LEACH协议能解决水声传感器网络分簇协议存在的能量问题,使网络的能量消耗更加均匀,并在一定程度上延长网络的生存期限。
