| As an emerging network architecture,Mobile Edge Network(MEN)is receiving extensive attention from academia and industry.Its core idea is to deploy computing and storage resources at the edge of the network closer to users,and use software-defined networking,and network function virtualization to flexibly allocate and expand resources,to realize the adaptation of network resources according to user demands.The development of MEN currently encounters two challenges:(1)The movement of users leads to a highly dynamic spatial-temporal distribution of user demands,which not only increases the difficulty of demand perception,but also puts forward higher requirements on the efficiency and real-time performance of resource adaption.(2)The perception of user demands will bring the risk of privacy leakage.To satisfy the actual needs of mobile edge networks and solve the above challenges,this thesis conducts in-depth research on mobility pattern mining,user demand perception,resource collaborative optimization,and user privacy protection,and provides effective solutions to achieve the purpose of improving network service quality,reducing network resource overhead,and ensuring user privacy and security.The specific contributions are summarized as follows:(1)Group Mobility Pattern Mining Based on Trajectory RepresentationTo find the spatio-temporal correlations of user demands,this thesis proposes a group mobility pattern mining method based on trajectory representation,which realizes accurate trajectory similarity computation and efficient popular routes mining in massive trajectory datasets.Specifically,1)Aiming at the problem of inaccurate and incomplete trajectory representation in existing trajectory representation models,this thesis proposes a novel Contrastive Self-Supervised Trajectory Representation Model.This model has two characteristics: a)a contrastive loss function is designed to capture both trajectorylevel features and point-level features in the trajectories;b)a self-supervised data augmentation method is proposed.By combining dropping,distorting,and negative sampling,this method can efficiently generate training data The experimental results on real trajectory datasets show that CSTRM achieves a maximum accuracy improvement of 15% over existing models in three trajectory similarity computation tasks,and the distribution of trajectory representation vectors is wider than existing models.2)Based on the accurate representations obtained by CSTRM,a clustering-based group mobility pattern mining algorithm is proposed,which can efficiently discover the popular routes in the city.(2)Demand-aware resource collaborative optimization algorithmAiming at the problem of mismatch between user demands and resource deployment in the mobile edge network scenario where users move at high speed,this thesis designs a demand-aware resource collaborative optimization algorithm.This algorithm consists of two parts: 1)User demand prediction model.To accurately predict the change in user demand caused by multiple movements of users in one cache update cycle,this thesis proposes a multi-step user mobility prediction model and a behavior-based user request prediction model.2)Collaborative optimization algorithm.Considering the spatialtemporal distribution of user demands,the matching strategy and content deployment strategy of the BBUs and RRHs are studied to maximize the system cache hit rate under the constraints of BBU utility and network cache space.To this end,this thesis first formulates the problem as an integer linear programming problem and proves its NP-hard property.Then,a popular routes-based static optimization strategy and a greedy mergebased dynamic optimization strategy are proposed to efficiently solve this optimization problem.The simulation results on real data show that: 1)Compared with popularitybased and preference-based models,the proposed user demand model can accurately predict the spatial-temporal distribution of user demand and obtain 48.1% and 51.1%improvement on cache hit rate.2)The proposed static and dynamic optimization strategies achieve at least 7% and 2.6% cache hit rate improvement over the optimal baseline strategy,respectively.(3)Trajectory Privacy Protection Algorithm in Spatio-temporal Data StreamTo accurately sense the spatio-temporal distribution of user demands,the mobile edge network continuously aggregates user data and counts the number of users in each region and the number of visits to each content.To prevent attackers from inferring the user’s trajectory from these aggregated spatio-temporal data streams,this thesis designs a trajectory privacy protection framework.Aiming at the problem that the existing privacy protection models ignore the spatial dimension and cannot strike a balance between trajectory privacy protection and stream data utility(accuracy),this thesis proposes a trajectory privacy protection framework based on a two-dimensional sliding window method.This framework has three characteristics: 1)spatial-temporal window.Considering the spatio-temporal continuity of user trajectory,a spatio-temporal window(w,n)is defined by combining the n-range spatial window and the w-slot temporal window;2)(w,n)-difference privacy framework based on sliding window methodology.This framework includes two unique designs: a)By perturbing the data in the spatiotemporal window,it ensures that the attacker cannot infer any spatio-temporal sequences within this window;b)By sliding the spatio-temporal window on the data stream,this model ensures the safety of any trajectory sequences within w consecutive timestamps and n-range places.3)two(w,n)-differential privacy algorithms.To improve data utility,two algorithms detect the spatial-temporal dynamics of the data stream and adaptively perturb and publish the data stream.Theoretical analysis shows that two algorithms satisfy the(w,n)-differential privacy.The experimental results show that the algorithms have achieved at least 18% and 30% improvement in data utility while ensuring trajectory privacy. |
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