能量采集wsn的智能应用

T. V. Prabhakar, S. Devasenapathy, H. Jamadagni, R. V. Prasad
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引用次数: 12

摘要

利用采集能量供电的无线传感器网络(WSN)的运行受到瞬时功率的限制。由于网络中各个节点的能源可用性可能不同,因此网络设置和协作是一项重要的任务。同时,在能量过剩的情况下,存在令人兴奋的节点协作可能性;电池驱动的传感器网络通常不可行。传感、计算、存储和通信等操作是实现任何传感器网络共同目标所必需的。在本文中,我们设计并实现了一个使用决策引擎的智能应用程序,并将其转化为一个能量匹配的应用程序。这些结果是基于使用IRIS卫星在太阳能上运行的测量结果。我们已经不再使用电池;取而代之的是使用低泄漏的超级电容器来储存收集的能量。决策引擎利用两部分数据来提供建议。首先,基于历史的能量预测模型帮助发动机获得有关输入能量的信息。第二个输入是操作的能源成本数据库。能源驱动的决策引擎计算能源预算,并推荐可能的最佳操作集。在能量过剩的情况下,决策引擎混杂地嗅探邻居,从邻居那里寻找所有可能的数据。这些数据包括邻居的能量水平和传感器数据。有了这些数据,节点建立了详细的数据关联,从而增强了协作,例如代表低能量条件下休眠的节点填补数据空白。结果令人鼓舞。与基础应用程序相比,运行智能应用程序的传感器节点的节点和网络生命周期明显更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart applications for energy harvested WSNs
A Wireless Sensor Network (WSN) powered using harvested energies is limited in its operation by instantaneous power. Since energy availability can be different across nodes in the network, network setup and collaboration is a non trivial task. At the same time, in the event of excess energy, exciting node collaboration possibilities exist; often not feasible with battery driven sensor networks. Operations such as sensing, computation, storage and communication are required to achieve the common goal for any sensor network. In this paper, we design and implement a smart application that uses a Decision Engine, and morphs itself into an energy matched application. The results are based on measurements using IRIS motes running on solar energy. We have done away with batteries; instead used low leakage super capacitors to store harvested energy. The Decision Engine utilizes two pieces of data to provide its recommendations. Firstly, a history based energy prediction model assists the engine with information about in-coming energy. The second input is the energy cost database for operations. The energy driven Decision Engine calculates the energy budgets and recommends the best possible set of operations. Under excess energy condition, the Decision Engine, promiscuously sniffs the neighborhood looking for all possible data from neighbors. This data includes neighbor's energy level and sensor data. Equipped with this data, nodes establish detailed data correlation and thus enhance collaboration such as filling up data gaps on behalf of nodes hibernating under low energy conditions. The results are encouraging. Node and network life time of the sensor nodes running the smart application is found to be significantly higher compared to the base application.
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