Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recent years, there have been several kinds of energy harvesting networks containing some tiny devices, such as ambient backscatter [1], ring [2] and renewable sensor networks [3]. During the energy harvesting, the networks suffer from the energy heterogeneity, dynamics and prediction hardness because the access to natural resources is often spatiotemporal different and timely changing among the devices. Meanwhile, the charging efficiency is quite low especially when the por of the harvested energy is ak. It results in the energy waste to store the harvested energy indirectly. These features bring challenging and interesting issues on efficient allocation of the harvested energy.  studies the stochastic duty cycling by considering these features with the objective characterized by maximizing the common active time.  consider two cases: offline and online stochastic duty cycling. For the offline case,  design an optimal solution: offline duty cycling algorithm. For the online case,  design an online duty cycling algorithm, which achieves the approximation ratio with at least equation where γ is the probability able to harvest energy.  also evaluate our algorithms with the experiment on a real energy harvesting network. The experiment results show that the performance of the online algorithm can be very close to the offline algorithm.
['Jianhui Zhang', 'Mengmeng Wang', 'Zhi Li']
I am writing a research paper on a idea Stochastic duty cycling for heterogenous energy harvesting networks
[{"from": "human", "value": "I am writing a research paper on a idea Stochastic duty cycling for heterogenous energy harvesting networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recent years, there have been several kinds of energy harvesting networks containing some tiny devices, such as ambient backscatter [1], ring [2] and renewable sensor networks [3]. During the energy harvesting, the networks suffer from the energy heterogeneity, dynamics and prediction hardness because the access to natural resources is often spatiotemporal different and timely changing among the devices. Meanwhile, the charging efficiency is quite low especially when the por of the harvested energy is ak. It results in the energy waste to store the harvested energy indirectly. These features bring challenging and interesting issues on efficient allocation of the harvested energy.  studies the stochastic duty cycling by considering these features with the objective characterized by maximizing the common active time.  consider two cases: offline and online stochastic duty cycling. For the offline case,  design an optimal solution: offline duty cycling algorithm. For the online case,  design an online duty cycling algorithm, which achieves the approximation ratio with at least equation where \u03b3 is the probability able to harvest energy.  also evaluate our algorithms with the experiment on a real energy harvesting network. The experiment results show that the performance of the online algorithm can be very close to the offline algorithm."}]