隨著行動網路和適地性服務的普及，使用者可以透過其行動裝置基於地理位置搜尋並下載鄰近的數個興趣點內容。而地理位置相鄰的使用者通常會下載到重覆甚至於完全相同的興趣點內容。此各自下載興趣點內容的情況對於4G/5G行動網路以及網際網路造成了極大的負荷以及資源的浪費。為了解決上述問題，這篇論文提出了一種應用於鄰近式服務之分群方法。首先將使用者的行動裝置分成數個群組並在每個群組中選擇一個組頭，由組頭負責透過需收費的4G/5G行動網路下載該群組所需之鄰近興趣點內容，再透過免費的WiFi無線網路以裝置到裝置通訊方式分享下載的興趣點內容給其群組成員。由於使用者會不斷移動，其行動裝置所需下載並暫存的興趣點內容也會依據其地理位置變更而動態更新。因此不同於傳統考慮到行動裝置的連通性以及中心性等特性所設計出的分群方法，這篇論文提出的分群方法還考慮了各個行動裝置(i)被選擇為組頭時所能減少的4G/5G行動網路資料下載量、(ii)透過WiFi無線網路連接的行動裝置數量、以及(iii)當下剩餘的電池電量。除此之外考慮到公平性，也就是避免某個行動裝置一直被選擇為組頭負責下載和分享興趣點內容，這篇論文還提出一個計點機制，被選擇為組頭行動裝置可以賺取點數，其群組成員需支付點數。在系統架構上，這篇論文採用了行動邊緣運算式的網路架構。在每個4G/5G基地台旁部署行動邊緣運算伺服器，由此行動邊緣運算伺服器(i)從雲端伺服器中暫存興趣點內容以及(ii)將連接上其所屬之4G/5G基地台的行動裝置分群。實驗模擬結果顯示這篇論文提出的分群方法相對於傳統分群方法在(i)減少4G/5G行動網路資料下載量以及(ii)平衡系統中各個行動裝置的電池電量消耗方面都有較佳的表現。

A location-based service (LBS) application normally allows its users to download Point of Interests’ (POIs’) contents nearby using their user equipments (UEs). Thus, users who are proximate with each other usually download similar or even the same POIs’ contents, which causes the resource waste and heavy load in Internet and 4G/5G cellular network if each user has the downloading individually. To tackle the aforementioned problem, this work proposed a clustering method that groups UEs into several clusters and select a cluster head for each cluster to be in charge of downloading POIs’ contents nearby through the charged 4G/5G cellular network and then forwarding the downloaded POIs’ contents to cluster members through the free charged Device-to-Device (D2D) communication, e.g., WiFi wireless network, to achieve the goal of proximate sharing, i.e., the Proximity Service (ProxSe). Since the considered users are moving and each user’s UE needs to download and cache the k-nearest POIs’ contents in the n-kilometer range, i.e., the downloaded and cached POIs’ contents need to be updated dynamically depending on the user’s current location, the corresponding clustering method is different from the traditional clustering methods, which considered centrality, connectivity, etc. To meet the aforementioned characteristics, the proposed clustering method considers (i) the reduced amount of the downloaded data volume of POIs’ contents using 4G/5G cellular network when a specific UE plays the role of the cluster head, (ii) the WiFi connectivity of the UE, and (iii) the remaining battery power of the UE to do clustering. Additionally, to achieve the fairness concern, the proposed clustering method also considers the credit, for which a credit scheme that has the user whose UE plays the role of the cluster head to earn credit and those users whose UEs play the role of the cluster member to pay credit was proposed in this work. This work adopted the Mobile Edge Computing (MEC) paradigm and architecture, in which a MEC server is associated with a 4G/5G BS. The MEC server can (i) cache POIs’ contents from the remote cloud server and (ii) execute the proposed clustering method to group users’ UEs that attached to the corresponding BS. Then, the cluster head of each cluster needs to download POIs’ contents from the MEC server through 4G/5G cellular network and forward the downloaded POIs’ contents to cluster members that belong to the corresponding cluster through WiFi wireless network. The simulation results have shown that the proposed clustering method has the better performance on (i) the reduced amount of the downloaded data volume of POIs’ contents and (ii) the consumed battery power.

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