延遲容忍網路中注重使用者偏好的資料散布機制

Abstract

由於行動裝置的興起與普遍化，使用者期望可以透過裝置之上的短距離傳輸媒介，如Wifi或是藍芽(Bluetooth)，在與其他使用者接近的時候能夠互相的交換多媒體檔案或是資訊；然而過去在這種延遲容忍網路下的資料散布研究大多注重在單一資料的散布並且沒有考慮到使用者對於不同檔案的喜好程度，因此在本篇論文中，我們提出一個注重使用者偏好，在延遲容忍網路中最大化使用者效用的檔案傳播機制。 而因為在這樣的行動社群網路中的網路連線狀態並不穩定，當使用者在相遇時間有限的情況之下，必須考慮到未來所產生的效益，選擇對整個網路最有效益的資料進行傳送來滿足所有的使用者。 我們把這樣的問題表示成一個最大權二分圖完美匹配的問題，並且提出使用者未來效用貢獻的估算方法。 透過模擬驗證的方式，我們所提出的方式比起以往單純考慮機率或是沒有考慮未來貢獻的方式，的確可以提高整體使用者的平均效用。

As mobile devices have become more ubiquitous, mobile users increasingly expect to utilize proximity-based connectivity, e.g., WiFi and Bluetooth, to opportunistically share multimedia content based on their personal preferences. However, many previous studies investigate content dissemination protocols that distribute a single object to as many users in an opportunistic mobile social network as possible without considering user preference. In this paper, we propose PrefCast, a preference-aware content dissemination protocol that targets on maximally satisfying user preference for content objects. Due to non-persistent connectivity of users in a mobile social network, when a user meets users for a limited contact duration, it needs to efficiently disseminate a suitable set of objects that can bring possible future contacts a high utility (the quantitative metric of preference satisfaction). We formulate such a problem as a maximum-utility forwarding model, and propose an algorithm that enables each user to predict how much utility it can contribute to possible future contacts and solve its optimal forwarding schedule in a distributed manner. Our trace-based evaluation shows that PrefCast can produce a 18:5% and 25:2% higher average utility than the protocols that only consider contact frequency and preference of local contacts, respectively.