Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents scheduling strategies for sensing workload in wireless sensor networks using Divisible Load Theory (DLT), which offers a tractable model and realistic approach to investigate optimal scheduling issues in distributed systems. Due to the limited energy resource it is desirable that a sensor network can complete tasks as fast as possible. Sensor nodes are coordinated to perform measuring, transmitting, and processing data. Two closely related network models are presented to illustrate how the workload is scheduled among sensor nodes so that the finish time is minimized. Closed-form solutions are derived to achieve the optimization if the source node satisfies certain utility rate, which is used to evaluate the informative ratio of sensory data. Furthermore,  present the energy model for sensor nodes in the multi-hop multi-source network topology. Finally, simulation results are presented to demonstrate the effects of different parameters such as the number of sensor nodes, measurement, communication, and processing speed on the finish time and energy consumption.
['Xiaolin Li', 'Xinxin Liu', 'Hui Kang']
I am writing a research paper on a idea Sensing Workload Scheduling in Sensor Networks Using Divisible Load Theory
[{"from": "human", "value": "I am writing a research paper on a idea Sensing Workload Scheduling in Sensor Networks Using Divisible Load Theory"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents scheduling strategies for sensing workload in wireless sensor networks using Divisible Load Theory (DLT), which offers a tractable model and realistic approach to investigate optimal scheduling issues in distributed systems. Due to the limited energy resource it is desirable that a sensor network can complete tasks as fast as possible. Sensor nodes are coordinated to perform measuring, transmitting, and processing data. Two closely related network models are presented to illustrate how the workload is scheduled among sensor nodes so that the finish time is minimized. Closed-form solutions are derived to achieve the optimization if the source node satisfies certain utility rate, which is used to evaluate the informative ratio of sensory data. Furthermore,  present the energy model for sensor nodes in the multi-hop multi-source network topology. Finally, simulation results are presented to demonstrate the effects of different parameters such as the number of sensor nodes, measurement, communication, and processing speed on the finish time and energy consumption."}]