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
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.
