Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To enable the intelligent management of Smart City and improve overall social lfare, it is desirable for the status of infrastructures detected and reported by intelligent devices embedded in them to be forwarded to the data centers. Using “SCmules” such as taxis, to opportunistically communicate with intelligent devices and collect data from the sparse networks formed by them in the process of moving is an economical and effective way to achieve this goal. , the social lfare data collection paradigm SWDCP-SCmules data collection framework is proposed to collect data generated by intelligent devices and forward them to data centers, in which “SCmules” are data transmitters picking up data from nearby intelligent devices and then store-carry-forwarding them to nearby data centers via short-range wireless connections in the process of moving. Because of the storage limitations, “SCmules” need to igh the value of data and select some less valuable data to discard when necessary. To quantify the value of data and find a ll-performed selection strategy, the concept of priority is introduced to the SWDCP-SCmules scheme, and then, the simulated annealing for priority assignment SA-PA algorithm is proposed to guide the priority assignment. The SA-PA algorithm is a universal algorithm that can improve the performance of SWDCP-SCmules scheme by finding better priority assignment with respect to various optimization targets, such as maximizing collection rate or minimizing redundancy rate, in which priority assignment problem is converted into an optimization problem and simulated annealing is used to optimize the priority assignment. From the perspective of machine learning, the process of optimization is equal to automatically learn social-aware patterns from past GPS trajectory data. Experiments based on real GPS trajectory data of taxis in Beijing are conducted to show the effectiveness and efficiency of SWDCP-SCmules scheme and SA-PA algorithm.
['Zhipeng Tang', 'Anfeng Liu', 'Changqin Huang']
I am writing a research paper on a idea Social-Aware Data Collection Scheme Through Opportunistic Communication in Vehicular Mobile Networks
[{"from": "human", "value": "I am writing a research paper on a idea Social-Aware Data Collection Scheme Through Opportunistic Communication in Vehicular Mobile Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To enable the intelligent management of Smart City and improve overall social lfare, it is desirable for the status of infrastructures detected and reported by intelligent devices embedded in them to be forwarded to the data centers. Using \u201cSCmules\u201d such as taxis, to opportunistically communicate with intelligent devices and collect data from the sparse networks formed by them in the process of moving is an economical and effective way to achieve this goal. , the social lfare data collection paradigm SWDCP-SCmules data collection framework is proposed to collect data generated by intelligent devices and forward them to data centers, in which \u201cSCmules\u201d are data transmitters picking up data from nearby intelligent devices and then store-carry-forwarding them to nearby data centers via short-range wireless connections in the process of moving. Because of the storage limitations, \u201cSCmules\u201d need to igh the value of data and select some less valuable data to discard when necessary. To quantify the value of data and find a ll-performed selection strategy, the concept of priority is introduced to the SWDCP-SCmules scheme, and then, the simulated annealing for priority assignment SA-PA algorithm is proposed to guide the priority assignment. The SA-PA algorithm is a universal algorithm that can improve the performance of SWDCP-SCmules scheme by finding better priority assignment with respect to various optimization targets, such as maximizing collection rate or minimizing redundancy rate, in which priority assignment problem is converted into an optimization problem and simulated annealing is used to optimize the priority assignment. From the perspective of machine learning, the process of optimization is equal to automatically learn social-aware patterns from past GPS trajectory data. Experiments based on real GPS trajectory data of taxis in Beijing are conducted to show the effectiveness and efficiency of SWDCP-SCmules scheme and SA-PA algorithm."}]