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首页> 外文会议>Proceedings of the 2016 IEEE International Conference on Wireless Communications, Signal Processing and Networking >Energy aware optimal clustering and reliable routing based on Markov model in Wireless Sensor Networks
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Energy aware optimal clustering and reliable routing based on Markov model in Wireless Sensor Networks

机译：无线传感器网络中基于马尔可夫模型的能量感知最优聚类和可靠路由

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Wireless sensor networks are constituted out of a large number of sensor nodes with limited energy resources. Severe shortage of onboard energy resource of WSN necessitates the combined solutions to its energy challenges. We propose an effective, method of clustering and reliable packet routing for the mobile nodes of sensor networks. Typically when nodes show some mobility in deployed network area, localization becomes complex which makes the clustering process even more complicated. In most of the existing works, Bayesian method [12] of predicting the future changes in nodes position is employed, wherein changes in states are based on prior probability distribution. However there is no influence of prior positions of sensor nodes on future states of its position. The node's movement to next location can be predicted using its current state of location and hence node's localization is being modeled as markov chains in our work. Normally the energy levels of sensor nodes can be configured to exhibit discrete dynamic energy values based on its different working modes like active, listen and sleep modes. We also focus an effective energy conservation by sending few number of energy abundant nodes to active states to support reliable data transmission. In this paper, we present an efficient reduction of energy consumption plus reliable packet delivery based on optimal cluster formation and markov model for mobile sensor networks. For implementing optimal clusters, we meticulously use k-medoids algorithm as a primary method and next based on node's communication cost, residual battery energy and its movements, transition probability matrix is obtained. This matrix is further utilized to forecast the reliable path for data transmission from source to destination.
机译：无线传感器网络由大量具有有限能源资源的传感器节点组成。 WSN的船上能源严重短缺，因此需要结合解决方案来应对其能源挑战。我们为传感器网络的移动节点提出了一种有效的聚类方法和可靠的分组路由选择。通常，当节点在已部署的网络区域中显示出一定的移动性时，本地化会变得很复杂，这会使群集过程变得更加复杂。在大多数现有工作中，采用了预测节点位置未来变化的贝叶斯方法[12]，其中状态的变化基于先验概率分布。但是，传感器节点的先前位置对其位置的未来状态没有影响。可以使用节点的当前位置状态来预测其移动到下一位置，因此在我们的工作中，节点的定位被建模为马尔可夫链。通常，传感器节点的能级可以配置为根据其不同的工作模式（例如活动模式，侦听模式和睡眠模式）表现出离散的动态能量值。我们还将通过向活动状态发送少量能量丰富的节点以支持可靠的数据传输来关注有效的节能。在本文中，我们基于移动传感器网络的最佳集群形成和马尔可夫模型，提出了一种有效的能耗降低方法以及可靠的数据包传递方法。为了实现最佳集群，我们精心地使用k-medoids算法作为主要方法，然后基于节点的通信成本，剩余电池能量及其移动，获得转换概率矩阵。该矩阵还用于预测从源到目的地的数据传输的可靠路径。

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