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A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks

机译：大型分布式传感器网络的基于矩阵的主动数据中继算法

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摘要

In large-scale distributed sensor networks, sensed data is required to be relayed around the network so that one or few sensors can gather adequate relative data to produce high quality information for decision-making. In regards to very high energy-constraint sensor nodes, data transmission should be extremely economical. However, traditional data delivery protocols are potentially inefficient relaying unpredictable sensor readings for data fusion in large distributed networks for either overwhelming query transmissions or unnecessary data coverage. By building sensors’ local model from their previously transmitted data in three matrixes, we have developed a novel energy-saving data relay algorithm, which allows sensors to proactively make broadcast decisions by using a neat matrix computation to provide balance between transmission and energy-saving. In addition, we designed a heuristic maintenance algorithm to efficiently update these three matrices. This can easily be deployed to large-scale mobile networks in which decisions of sensors are based on their local matrix models no matter how large the network is, and the local models of these sensors are updated constantly. Compared with some traditional approaches based on our simulations, the efficiency of this approach is manifested in uncertain environment. The results show that our approach is scalable and can effectively balance aggregating data with minimizing energy consumption.
机译：在大规模分布式传感器网络中，需要在网络周围中继感测到的数据，以便一个或几个传感器可以收集足够的相对数据，以生成用于决策的高质量信息。对于能量约束非常高的传感器节点，数据传输应该非常经济。但是，传统的数据传递协议在无法传递庞大的查询传输或不必要的数据覆盖范围时，无法可靠地传递不可预测的传感器读数以进行大型分布式网络中的数据融合。通过根据传感器先前传输的数据以三个矩阵建立传感器的本地模型，我们开发了一种新颖的节能数据中继算法，该算法允许传感器通过使用整洁的矩阵计算主动做出广播决策，从而在传输与节能之间取得平衡。此外，我们设计了一种启发式维护算法来有效地更新这三个矩阵。可以轻松地将其部署到大型移动网络中，无论网络有多大，传感器的决策均基于其本地矩阵模型，并且这些传感器的本地模型会不断更新。与基于我们的仿真的一些传统方法相比，该方法的效率在不确定的环境中得以体现。结果表明，我们的方法是可扩展的，可以有效地在聚合数据与最小化能耗之间取得平衡。

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