With the advent of information age, mobile devices have become one of the major convenient equipment that aids people’s daily office activities such as academic research, one of whose major tasks is to check the repetition rate or similarity among different English literatures. Traditional literature similarity checking solutions in cloud paradigm often call for intensive computational cost and long waiting time. To tackle this issue, in this paper, we modify the traditional literature similarity checking solution in cloud paradigm to make it suitable for the light-weight mobile edge environment. Furthermore, we put forward a lightweight similarity checking approach $$\mathrm {SC_{MEC}}$$ SC MEC for English literatures in mobile edge computing environment. To validate the advantages of $$\mathrm {SC_{MEC}}$$ SC MEC , we have designed massive experiments on a dataset. The reported experimental results show that $$\mathrm {SC_{MEC}}$$ SC MEC can deliver a satisfactory similarity checking result of literatures compared to other existing approaches.
Traffic flow prediction is an important part of intelligent transportation systems (ITS). However, sensor failures or the transmission distortion often occur in the process of data acquisition, which will inevitably cause the loss or abnormality of traffic flow data transmitted to the edge server. In this situation, it is necessary to share traffic flow data among different platforms. However, existing traffic flow prediction methods are facing two challenges in the process of traffic flow data sharing. First, user privacy is often leaked in the process of sharing traffic data on various platforms. Moreover, with the continuous updating of data, the efficiency and scalability of data sharing between different platforms will become lower and lower. In view of the above challenges, in this paper, we propose a novel prediction method for the missing traffic flow data caused by abnormal sensors, named $$ASMVP_{distr-LSH}$$ A S M V P d i s t r - L S H based on distributed locality-sensitive hashing (LSH) technique. At last, a case study is presented to illustrate the feasibility and effectiveness of our approach $$ASMVP_{distr-LSH}$$ A S M V P d i s t r - L S H .
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