Mobility-driven user-centric AP clustering in mobile edge computing-based ultra-dense networks

ultra-Dense Network(UDN)has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas,in which the density of Access Points(APs)is increased up to the point where it is comparable with or surpasses the density of active mobile users.In order to mitigate inter-AP interference and improve spectrum efficiency,APs in UDNs are usually clustered into multiple groups to serve different mobile users,respectively.However,as the number of APs increases,the computational capability within an AP group has become the bottleneck of AP clustering.In this paper,we first propose a novel UDN architecture based on Mobile Edge Computing(MEC),in which each MEC server is associated with a user-centric AP cluster to act as a mobile agent.In addition,in the context of MEC-based UDN,we leverage mobility prediction techniques to achieve a dynamic AP clustering scheme,in which the cluster structure can automatically adapt to the dynamic distribution of user traffic in a specific area.Simulation results show that the proposed scheme can highly increase the average user throughput compared with the baseline algorithm using max-SINR user association and equal bandwidth allocation,while it guarantees at the same time low transmission delay.

A Solution to Knowledge Management in Networked Collaborative Design

Concerning the demands in networked collaborative innovative design, a knowledge-based collaborative design model is introduced the model of knowledge integration along with its relevant supporting techniques are presented. After illustrating the general knowledge search paradigm, a kind of dynamic user model is proposed to improve knowledge search efficiency. At last a short introduction of the system’s implementation is described.

MSDA:multi-subset data aggregation scheme without trusted third party

Data aggregation has been widely researched to address the privacy concern when data is published,meanwhile,data aggregation only obtains the sum or average in an area.In reality,more fine-grained data brings more value for data consumers,such as more accurate management,dynamic price-adjusting in the grid system,etc.In this paper,a multi-subset data aggregation scheme for the smart grid is proposed without a trusted third party,in which the control center collects the number of users in different subsets,and obtains the sum of electricity consumption in each subset,meantime individual user’s data privacy is still preserved.In addition,the dynamic and flexible user management mechanism is guaranteed with the secret key negotiation process among users.The analysis shows MSDA not only protects users’privacy to resist various attacks but also achieves more functionality such as multi-subset aggregation,no reliance on any trusted third party,dynamicity.And performance evaluation demonstrates that MSDA is efficient and practical in terms of communication and computation overhead.