دانلود رایگان مقاله شبکه های تحمل تاخیر وسایل نقلیه برای مدیریت داده های شبکه هوشمند با استفاده از محاسبات لبه تلفن همراه

With the widespread popularity and usage of  ICT around the world, there is increasing inter - est in replacing the traditional electric grid by the  smart grid in the near future. Many smart devic - es exist in the smart grid environment. These  devices may share their data with one another  using the ICT-based infrastructure. The anal - ysis of the data generated from various smart  devices in the smart grid environment is one of  the most challenging tasks to be performed as  it varies with respect to parameters such as size,  volume, velocity, and variety. The output of the  data analysis needs to be transferred to the end  users using various networks and smart applianc - es. But sometimes networks may become over - loaded during such data transmissions to various  smart devices. Consequently, significant delays  may be incurred, which affect the overall per - formance of any implemented solution in this  environment. We investigate the use of VDTNs  as one of the solutions for data dissemination  to various devices in the smart grid environment  using mobile edge computing. VDTNs use the  store-and-carry forward mechanism for message  dissemination to various smart devices so that  delays can be reduced during overloading and  congestion situations in the core networks. As  vehicles have high mobility, we propose mobile  edge network support assisted by the cloud envi - ronment to manage the handoff and the pro - cessing of large data sets generated by various  smart devices in the smart grid environment. In  the proposed architecture, most of the compu - tation for making decisions about charging and  discharging is done by mobile devices such as  vehicles located at the edge of the network (also  called mobile edge computing). The comput - ing and communication aspects are explored to  analyze the impact of mobile edge computing  on performance metrics such as message trans - mission delay, response time, and throughput to  the end users using vehicles as the mobile nodes.  Our empirical results demonstrate an improved  performance 10–15 percent increase in through - put, 20 percent decrease in response time, and 10  percent decrease in the delay incurred with our  proposed solution compared to existing state-of- the-art solutions in the literature.