Hao Jie, Chen Jing, Wang Ran, Zhuang Yi, Zhang Baoxian
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China.
Sensors (Basel). 2019 Jul 12;19(14):3090. doi: 10.3390/s19143090.
Maximizing the utility under energy constraint is critical in an Internet of Things (IoT) sensing service, in which each sensor harvests energy from the ambient environment and uses it for sensing and transmitting the measurements to an application server. Such a sensor is required to maximize its utility under the harvested energy constraint, i.e., perform sensing and transmission at the highest rate allowed by the harvested energy constraint. Most existing works assumed a sophisticated model for harvested energy, but neglected the fact that the harvested energy is random in reality. Considering the randomness of the harvested energy, we focus on the transmission scheduling issue and present a robust transmission scheduling optimization approach that is able to provide robustness against randomness. We firstly formulate the transmission scheduling optimization problem subject to energy constraints with random harvested energy. We then introduce a flexible model to profile the harvested energy so that the constraints with random harvested energy are transformed into linear constraints. Finally, the transmission scheduling optimization problem can be solved traditionally. The experimental results demonstrate that the proposed approach is capable of providing a good trade-off between service flexibility and robustness.
在物联网（IoT）传感服务中，在能量约束下最大化效用至关重要，其中每个传感器从周围环境中收集能量，并将其用于传感以及将测量数据传输到应用服务器。这种传感器需要在收集到的能量约束下最大化其效用，即在收集到的能量约束所允许的最高速率下进行传感和传输。大多数现有工作都假设了一个复杂的收集能量模型，但忽略了实际中收集到的能量是随机的这一事实。考虑到收集能量的随机性，我们专注于传输调度问题，并提出了一种鲁棒的传输调度优化方法，该方法能够针对随机性提供鲁棒性。我们首先制定了受随机收集能量的能量约束的传输调度优化问题。然后引入一个灵活的模型来描述收集到的能量，以便将具有随机收集能量的约束转化为线性约束。最后，可以按照传统方法解决传输调度优化问题。实验结果表明，所提出的方法能够在服务灵活性和鲁棒性之间提供良好的权衡。
