Effective Broadcasting and Caching Technique for Video on Demand over Wireless Network

The Reverse k Nearest Neighbor (RkNN) query is valuable for finding objects influenced by a specific object and is widely used in both scientific and commercial systems. However, the influence level of each object is unknown, information that is critical for some applications (e.g. target marketing). In this paper, we propose a new query type, Ordered Reverse k Nearest Neighbor (ORkNN), and make efforts to adapt it in an on-demand scenario. An Order-k Voronoi diagram based approach is used to answer ORkNN queries. In particular, for different values of k, we pre-construct only one Voronoi diagram. Algorithms on both the server and the clients are presented. We also present experimental results that suggest our proposed algorithms may have practical applications.

Section: Introductionmentioning

confidence: 99%

Ordered Reverse k Nearest Neighbor Search via On-demand Broadcast

2014

On OFDM Subcarrier Allocation Strategies for Soft Hand-off in Cellular Systems

2012

This paper deals with subcarrier allocation strategies for soft hand-over in OFDMA-based cellular systems. Two possible subcarrier allocation methods are considered for soft hand-over. One method is to use an identical subcarrier set between the two cells participating in the hand-over. The other is to use different subcarrier sets between the two cells. As expected, the different subcarrier strategy is better in terms of diversity order and BER than the identical subcarrier strategy. It will be shown that the BER performance difference between the two strategies is more noticeable with contiguous subcarrier allocation. But the different subcarrier strategy consumes twice more frequency resources than the other, and there is a trade-off between the two strategies in terms of BER and frequency resources. By considering the trade-off, we also propose a subcarrier allocation strategy for soft hand-over.

Joint Access Point Selection and Local Discriminant Embedding for Energy Efficient and Accurate Wi-Fi Positioning

2012

We propose a novel method for improving Wi-Fi positioning accuracy while reducing the energy consumption of mobile devices. Our method presents three contributions. First, we jointly and intelligently select the optimal subset of access points for positioning via maximum mutual information criterion. Second, we further propose local discriminant embedding algorithm for nonlinear discriminative feature extraction, a process that cannot be effectively handled by existing linear techniques. Third, to reduce complexity and make input signal space more compact, we incorporate clustering analysis to localize the positioning model. Experiments in realistic environments demonstrate that the proposed method can lower energy consumption while achieving higher accuracy compared with previous methods. The improvement can be attributed to the capability of our method to extract the most discriminative features for positioning as well as require smaller computation cost and shorter sensing time.