近年來行動點對點網路(Mobile peer to peer network)隨著無線網路技術的提升，關於資料散播上的應用隨之蓬勃發展。先前部份研究多著重在如何有效地減少傳輸量，然而仍然存在許多未解的挑戰。首先之前的研究多需藉由手機上的硬體(GPS)支援來取得使用者地理位置相關資訊，來達成演算法目標，由於需要這類硬體設備的支援，因此造成手持裝置額外的能源消耗，導致訊息無法有效的轉發給其他使用者，我們的方法不使用這類的硬體支援取去這些資訊，我們藉由觀察使用者周圍鄰居的變化程度，去觀察推測使用者所屬環境。其次，使用者會依據自己收到的資訊是否符合自己的興趣去調整自己的傳輸次數，當使用者收到越多感興趣的資料，就會在廣告區域中做出更多貢獻，使用者不再無條件的一直做出貢獻。另外為目前部份研究皆假設手持裝置能夠透過廣播(broadcast)的方式接收訊息，而我們的方法將使用群播(multicast)的方式傳輸資料，因為群播技術(multicast)較能在現實生活中實現，未來也可透過藍芽來實現此應用。根據實驗的結果 我們的方法比起先前的研究更能有效減少傳輸量，並且還是能有效率的將資料快速的散播出去。

We in this thesis explore a new data dissemination framework in Mobile P2P networks. Previous works in the literature usually elaborated upon the reduction of dissemination frequency in the network. However, many important and practical issues remain unresolved. First, the success of the system design usually relies on the support of other internal senor such as GPS and accelerator, causing the extra power consumption. We propose the NCS framework which observes user's neighborhood variation to estimate the environments and without using any internal sensor. In addition, the user preference is not considered, and each peer will receive and help to broadcast all messages whether the user is interested in. We consider preference aware disseminations to support various user preferences in the Mobile P2P network. The fairness issue is also considered in the model, meaning that users who receive more interesting messages should contribute more message retransmission than users who wonder to receive few messages. Finally, the current stage concentrates on applications of data dissemination in student communities. The NCS framework is built based on multicast protocol. The results show our performance is better than the basic method and previous work such as Optimized Gossiping.

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