Hierarchical Cross-Layer Soft Computation Model for WiMAX Networks and Its Electronic Sytem Level Verification

[[abstract]]本計畫成功地開發了第一年計畫所擬的階層式跨協定控制模型(Hierarchical Cross-Layer Control; HCLC)以及軟體排程器軟體元件。本成果報告簡介此成果包含已發表的SCI期刊、國際會議、全國性競賽以及專利，定義並找到WiMAX MAC層QoS遇到的問題，排程器的重要性，以及成功完成此排成器的設計。方法採用軟式HCLC控制，並說明 Lyapunov穩定性原則下，各階層控制器設計應遵循的法則。藉由此法則，除了能夠達到一般所謂的同類別公平性之外，還能保證跨類別公平性，並保證QoS。本計畫成果已發表論文中的實驗顯現出HCLC的創新性與優越性，並證明可以順利銜接第二年計畫所擬IEEE 802.16m以及第三年的矽智財實現與其電子系統層級驗證。本報告最後並說明計畫書所擬之外的額外發現。[[abstract]]This project successfully developed HCLC control model and software scheduler component for OFDM-based 802.16e-2005 Mobile WiMAX which was the original planed objective of the first year proposal. The technical report here introduces the solid project achievements and we have published them in a SCI journal, an international conference, and patent as well as awarded in a contest. The contents of the achievements include WiMAX MAC QoS challenge definition and identification, importance of good scheduler design, and the successful scheduler design. We exploit HCLC soft control in which design policies based on Lyapunov stability principle are applied. With the proposed policies, the scheduler achieves inter- and intra-class fairness as well as QoS guarantee. The published papers based on this project show that the HCLC is superior to the state-of-the-art cross-layer designs. Therefore, we have proved we are ready to continue the second and third years’’ projects where IEEE 802.16m scheduler and silicon intellectual property (SIP) with electronic system level (ESL) verification are respectively proposed. We finally introduce the discovery beyond the originally proposed.[[note]]NSC97-2221-E327-03

Linux USB裝置端之自動表圖資料讀取器

[[note]]IAC-99-11

Multi-Metric Cross-Layer Network Quality Management for Multiple Level Cloud Computing Environment (II)(III)

[[abstract]]本子計畫開發多層次雲端計算服務之階層式跨層服務品質(QoS)管理技術，以提昇服務的 各種效能，進而提昇使用者滿意度。計畫過程中首先建立階層式雲端計算架構，由用戶 端根據座標、需求、資源屬性等自主性組織建立羽雲層(cloudlet tier)並推舉其一為仲介 器，開始對外分享各種資訊與資源；數個cloudlets組織成私有小雲層(cloudling tier)，每 一cloudling有一broker管理；數個私有cloudlings可在組織成一團浮雲(cloud rack)，是為 cloud rack tier，由public cloud中建立Web 2.0 資料庫進行管理，以整合公共雲資源，最 上層為厚雲層(cloud bank tier)。利用這樣的階層式架構，我們將能涵蓋大範圍的資源， 並加以利用。這些資源將透過本計畫之技術，基於雲端計算QoS之限制以及需求，並搭 配各子計畫完成QoS管理。其中QoS效能之評量包含：私有雲端服務品質(Home Cloud QoS，HCQ)、解決方案堆疊QoS (Solution Stack QoS，SSQ)、雲間QoS (Inter-Cloud QoS，ICQ)、 協調資訊安全之QoS管理(Security with QoS，SecQ)，並搭配子計畫二動態配置虛擬機器 之輸出/輸入排程。在設計方法上，將參考過去在無線網路相關研究之成果「階層式跨 層控制」(HCLC)框架加以延伸，延伸方向符合雲端虛擬化之精神，將HCLC框架橫向延伸， 可以跨虛擬機器(Virtual Machines，VMs)以及跨多組私有雲和公共雲，並將跨協定溝通 機制延伸成為服務摘要(Service Request Profile, 簡稱request)與執行計畫(Execution Assignment Plan, 簡稱assignment)，藉此除有利私有雲之間的QoS溝通協調之外，更可擴 及公共雲QoS。在這樣的方法之下，我們以大型戶外活動應用做為技術開發成果之展示。 三年計畫(第一年已開始執行)將可以產出下列成果：第一年：已完成SSQ跨層控制數學模 型與穩定性證明以及cloudlet brokers與ADM cloudling brokers之request與task assignment 訊息傳遞模擬(100年11月)，即將完成：五項QoS效能指標之排程。第二年：跨私有雲且 跨層QoS控制數學模型與穩定性證明、連結羽雲層、私有雲仲介器閘道、與公共雲之3-tier 智慧型跨層QoS協調與控制、跨私有雲仲介器閘道五項QoS效能指標之排程。第三年： 跨公、私雲之間跨層QoS控制數學模型與穩定性證明、跨公、私雲之間仲介器閘道之智 慧型跨層QoS協調與控制、跨公、私雲之間五項QoS效能指標之排程，建構完整動態階 層式雲端計算架構(簡稱雲海)與大量測試。本子計畫尋求成功大學數位生活科技研究中 心、永洋科技股份有限公司之支援與配合，主訴求產生有用之專利(目前申請中)，而在 學術上、產業應用、以及跨領域人才培育上均可獲致豐富之成果。[[abstract]]This subproject is to develop hierarchical cross-layer quality of service (QoS) management technology for multi-level cloud computing and services. Thus, it provides multi-metric performance and promotes users’ satisfaction. We firstly build up hierarchical cloud computing architecture. According to coordinates and properties of request and resources, the user-end devices autonomously organize into a group called cloudlet (thus, this tier is called cloudlet tier) and elect a cloudlet broker to start information and resources sharing. A set of cloudlets forms a cloudling (cloudling tier) managed by a cloudling broker. The cloudlings are again organized into cloud racks (thus, cloud rack tier) and managed by using Web 2.0 database built in public cloud. Therefore, public cloud resources are included in this architecture. Finally, the top tier is the cloud bank tier which groups the racks. Exploiting this hierarchical architecture, our QoS management covers huge usable resources. During the development, we study QoS constraints and requirements in cloud computing environment and we cooperate with other subprojects to accomplish the QoS management. The QoS metrics are in multiple aspects which comprise Home Cloud QoS(HCQ), Solution Stack QoS(SSQ), Inter-Cloud QoS (ICQ), and Security-Compromised QoS (SecQ). The design methodology is extended from the Hierarchical Cross-Layer Control (HCLC) framework, which is the achievement of previous projects of years. The extension is based on the virtualzation in the cloud computing in which the QoS management crosses hozontally over multiple virtual machines as well as crosses over private and public clouds. In the HCLC extension, the signaling mechanism exploits Service Request Profile (Request) and Execution Assignment Plan (Assigment) for communication and coordination among private and public clouds. Based on this methodology, we demonstrate our technology development results in the application of large-scale outings. The project is to play in three years. In the first year, we’ve proved the stability of SSQ cross-layer control methematic model and accomplished request and task assignment message sequence simulation between cloudlet brokers and cloudling brokers (Nov. 2011). To be accomplished is the scheduling with performance in terms of the five QoS metrics. In the second year, we will prove the stability of inter-private cloudings cross-layer control methematic model, and accomplish the 3-tier intelligent QoS coordination and management covering cloudlet, cloudling, and cloud rack tiers according to the five QoS metrics. In the third year, we prove the stability of cross-layer control methematic model for QoS control among private and public clouds, accomplish intelligent cross-layer QoS coordination and control in the dynamic and hierarchical cloud computing architecture, and build up the five-QoS-metric testing and verification platform with large amount of test benchs. This subproject seeks the supports from Center for Research of E-life DIgital Technology (CREDIT) and Advance Multimedia Internet Technology (AMIT) Corporation. Thus, in the academic area, industrial applications, and human education we will gain various and solid achievements.[[note]]NSC101-2221-E327-02

Multi-Metric Cross-Layer Network Quality Management for Multiple Level Cloud Computing Environment(Ii)(Iii)

[[abstract]]本子計畫開發多層次雲端計算服務之階層式跨層服務品質(QoS)管理技術，以提昇服務的 各種效能，進而提昇使用者滿意度。計畫過程中首先建立階層式雲端計算架構，由用戶 端根據座標、需求、資源屬性等自主性組織建立羽雲層(cloudlet tier)並推舉其一為仲介 器，開始對外分享各種資訊與資源；數個cloudlets組織成私有小雲層(cloudling tier)，每 一cloudling有一broker管理；數個私有cloudlings可在組織成一團浮雲(cloud rack)，是為 cloud rack tier，由public cloud中建立Web 2.0 資料庫進行管理，以整合公共雲資源，最 上層為厚雲層(cloud bank tier)。利用這樣的階層式架構，我們將能涵蓋大範圍的資源， 並加以利用。這些資源將透過本計畫之技術，基於雲端計算QoS之限制以及需求，並搭 配各子計畫完成QoS管理。其中QoS效能之評量包含：私有雲端服務品質(Home Cloud QoS，HCQ)、解決方案堆疊QoS (Solution Stack QoS，SSQ)、雲間QoS (Inter-Cloud QoS，ICQ)、 協調資訊安全之QoS管理(Security with QoS，SecQ)，並搭配子計畫二動態配置虛擬機器 之輸出/輸入排程。在設計方法上，將參考過去在無線網路相關研究之成果「階層式跨 層控制」(HCLC)框架加以延伸，延伸方向符合雲端虛擬化之精神，將HCLC框架橫向延伸， 可以跨虛擬機器(Virtual Machines，VMs)以及跨多組私有雲和公共雲，並將跨協定溝通 機制延伸成為服務摘要(Service Request Profile, 簡稱request)與執行計畫(Execution Assignment Plan, 簡稱assignment)，藉此除有利私有雲之間的QoS溝通協調之外，更可擴 及公共雲QoS。在這樣的方法之下，我們以大型戶外活動應用做為技術開發成果之展示。 三年計畫(第一年已開始執行)將可以產出下列成果：第一年：已完成SSQ跨層控制數學模 型與穩定性證明以及cloudlet brokers與ADM cloudling brokers之request與task assignment 訊息傳遞模擬(100年11月)，即將完成：五項QoS效能指標之排程。第二年：跨私有雲且 跨層QoS控制數學模型與穩定性證明、連結羽雲層、私有雲仲介器閘道、與公共雲之3-tier 智慧型跨層QoS協調與控制、跨私有雲仲介器閘道五項QoS效能指標之排程。第三年： 跨公、私雲之間跨層QoS控制數學模型與穩定性證明、跨公、私雲之間仲介器閘道之智 慧型跨層QoS協調與控制、跨公、私雲之間五項QoS效能指標之排程，建構完整動態階 層式雲端計算架構(簡稱雲海)與大量測試。本子計畫尋求成功大學數位生活科技研究中 心、永洋科技股份有限公司之支援與配合，主訴求產生有用之專利(目前申請中)，而在 學術上、產業應用、以及跨領域人才培育上均可獲致豐富之成果。[[abstract]]This subproject is to develop hierarchical cross-layer quality of service (QoS) management technology for multi-level cloud computing and services. Thus, it provides multi-metric performance and promotes users’ satisfaction. We firstly build up hierarchical cloud computing architecture. According to coordinates and properties of request and resources, the user-end devices autonomously organize into a group called cloudlet (thus, this tier is called cloudlet tier) and elect a cloudlet broker to start information and resources sharing. A set of cloudlets forms a cloudling (cloudling tier) managed by a cloudling broker. The cloudlings are again organized into cloud racks (thus, cloud rack tier) and managed by using Web 2.0 database built in public cloud. Therefore, public cloud resources are included in this architecture. Finally, the top tier is the cloud bank tier which groups the racks. Exploiting this hierarchical architecture, our QoS management covers huge usable resources. During the development, we study QoS constraints and requirements in cloud computing environment and we cooperate with other subprojects to accomplish the QoS management. The QoS metrics are in multiple aspects which comprise Home Cloud QoS(HCQ), Solution Stack QoS(SSQ), Inter-Cloud QoS (ICQ), and Security-Compromised QoS (SecQ). The design methodology is extended from the Hierarchical Cross-Layer Control (HCLC) framework, which is the achievement of previous projects of years. The extension is based on the virtualzation in the cloud computing in which the QoS management crosses hozontally over multiple virtual machines as well as crosses over private and public clouds. In the HCLC extension, the signaling mechanism exploits Service Request Profile (Request) and Execution Assignment Plan (Assigment) for communication and coordination among private and public clouds. Based on this methodology, we demonstrate our technology development results in the application of large-scale outings. The project is to play in three years. In the first year, we’ve proved the stability of SSQ cross-layer control methematic model and accomplished request and task assignment message sequence simulation between cloudlet brokers and cloudling brokers (Nov. 2011). To be accomplished is the scheduling with performance in terms of the five QoS metrics. In the second year, we will prove the stability of inter-private cloudings cross-layer control methematic model, and accomplish the 3-tier intelligent QoS coordination and management covering cloudlet, cloudling, and cloud rack tiers according to the five QoS metrics. In the third year, we prove the stability of cross-layer control methematic model for QoS control among private and public clouds, accomplish intelligent cross-layer QoS coordination and control in the dynamic and hierarchical cloud computing architecture, and build up the five-QoS-metric testing and verification platform with large amount of test benchs. This subproject seeks the supports from Center for Research of E-life DIgital Technology (CREDIT) and Advance Multimedia Internet Technology (AMIT) Corporation. Thus, in the academic area, industrial applications, and human education we will gain various and solid achievements.[[note]]NSC101-2221-E327-02

Multi-Metric Cross-Layer Network Quality Management for Multiple Level Cloud Computing Environment (III)

[[abstract]]本子計畫調查多份重要行動雲端計算文獻確定本計畫符合行動雲端運算趨勢。於是第三 年將持續開發多層次雲端計算服務之階層式跨層服務品質(QoS)管理技術，以提昇服務 的各種效能，進而提昇使用者滿意度。計畫過程中首先建立階層式雲端計算架構，由用 戶端根據座標、需求、資源屬性等自主性組織建立羽雲層(cloudlet tier)並推舉其一為仲 介器，開始對外分享各種資訊與資源；數個 cloudlets 組織成私有小雲層(cloudling tier)， 每一 cloudling 有一 broker 管理；數個私有 cloudlings 可在組織成一團浮雲(cloud rack)， 是為 cloud rack tier，由 public cloud 中建立 Web 2.0 資料庫進行管理(例如採用 Facebook 和 Google 提供之 API)，以整合公共雲資源，最上層為厚雲層(cloud bank tier)。利用這 樣的階層式架構，我們將能涵蓋大範圍的資源，並加以利用。這些資源將透過本計畫之 技術，基於雲端計算 QoS 之限制以及需求，並搭配各子計畫完成 QoS 管理。其中 QoS 效能之評量包含：私有雲端服務品質(Home Cloud QoS，HCQ)、解決方案堆疊 QoS (Solution Stack QoS，SSQ)、雲間 QoS (Inter-Cloud QoS，ICQ)、協調資訊安全之 QoS 管 理(Security with QoS，SecQ)，並搭配子計畫二動態配置虛擬機器之輸出/輸入排程。在 設計方法上，將參考過去在無線網路相關研究之成果「階層式跨層控制」(HCLC)框架 加以延伸，延伸方向符合雲端虛擬化之精神，將 HCLC 框架橫向延伸，可以跨虛擬機器 (Virtual Machines，VMs)以及跨多組私有雲和公共雲，並將跨協定溝通機制延伸成為服 務摘要(Service Request Profile, 簡稱 request)、執行計畫(Execution Assignment Plan, 簡稱 assignment)、以及服務回應訊息(Service Notifications/Grants Messages)，藉此除有利私有 雲之間的 QoS 溝通協調之外，更可擴及公共雲 QoS。在這樣的方法之下，我們修改原 大型戶外活動應用情境為：第一、分享多媒體數位內容 、分享多媒體數位內容，以及第二、與其他使用者分享 、與其他使用者分享 自己 LIVE 活動實況，以做為技術開發成果之展示。三年計畫將可以產出下列成果：第 一年：已完成 SSQ 跨層控制數學模型與穩定性證明以及 cloudlet brokers 與 cloudling brokers 之 request 與 task assignment 訊息傳遞模擬。第二年進行中包含：動態組織與連 結 cloudlets、cloudling、與 cloud rack 之 3-tier 智慧型跨層 QoS 協調與控制，以及跨 3-tier 行動雲端運算五項 QoS 效能指標之排程。第三年預計完成：1. 整合社群網路與使用者 情境之多模式感測與 SLA 產生技術(子計畫一為主、子計畫二、四搭配)；2. 基於階層 式動態 brokering 之網路虛擬化技術(子計畫三為主決策 broker 選擇、子計畫一提供 context、子計畫二提供運算能力選項、子計畫四提供 trust 目的地選項)；3. 虛擬機器之 Hot-spot detection, VM consolidation, and Task Handoff 技術(子計畫二為主、子計畫一提 供 context、子計畫三 CCS 控制、子計畫四提供 handoff 目的地選項)；4. 使用者群組 Self-contained AAA 技術(子計畫四為主、子計畫一、二、三搭配)。效能面以滿足 SSQ, HCQ 之外、加強 SecQ，並完成 Inter-Cloud QoS (ICQ)、Energy Utility Quality (EUQ)。本子計 畫尋求成功大學數位生活科技研究中心、永洋科技股份有限公司之支援與配合，主訴求 產生有用之專利(目前申請兩件)以及實體有用之產品應用，而在學術上、產業應用、以 及跨領域人才培育上均可獲致豐富之成果。[[abstract]]We survey the state-of-the-art contributions on mobile cloud computing to confirm that theis project meet the global trend of mobile cloud computing. Therefore, in the 3 rd year we continue developing hierarchical cross-layer quality of service (QoS) management technology for multi-level cloud computing and services. Thus, it provides multi-metric performance and promotes users’ satisfaction. We firstly build up hierarchical cloud computing architecture. According to coordinates and properties of request and resources, the user-end devices autonomously organize into a group called cloudlet (thus, this tier is called cloudlet tier) and elect a cloudlet broker to start information and resources sharing. A set of cloudlets forms a cloudling (cloudling tier) managed by a cloudling broker. The cloudlings are again organized into cloud racks (thus, cloud rack tier) and managed by using Web 2.0 database built in public cloud (such as using APIs provided by Facebook and Google). Therefore, public cloud resources are included in this architecture. Finally, the top tier is the cloud bank tier which groups the racks. Exploiting this hierarchical architecture, our QoS management covers huge usable resources. During the development, we study QoS constraints and requirements in cloud computing environment and we cooperate with other subprojects to accomplish the QoS management. The QoS metrics are in multiple aspects which comprise Home Cloud QoS(HCQ), Solution Stack QoS(SSQ), Inter-Cloud QoS (ICQ), and Security-Compromised QoS (SecQ). The design methodology is extended from the Hierarchical Cross-Layer Control (HCLC) framework, which is the achievement of previous projects of years. The extension is based on the virtualzation in the cloud computing in which the QoS management crosses hozontally over multiple virtual machines as well as crosses over private and public clouds. In the HCLC extension, the signaling mechanism exploits Service Request Profile (Request), Execution Assignment Plan (Assigment), and Service Notifications/Grants Messages for communication and coordination among private and public clouds. Based on this methodology, we change our demonstrating scenarios from large-scale outings to first, multimedia digital content sharing and second, personal live activities sharing as reviewers commented that the application scenario should be narrowed to be easier to demonstrate the results. The project is to play in three years. In the first year, we’ve proved the stability of SSQ cross-layer control methematic model and accomplished request and task assignment message sequence simulation between cloudlet brokers and cloudling brokers. To be accomplished is the scheduling with performance in terms of the five QoS metrics. In the second year, we are performing dynamic cloudlet grouping, cloudling brokers’ request profile and assignment messaging, as well as dynamic resource list aggregation for accomplishing the 3-tier intelligent QoS coordination and management covering cloudlet, cloudling, and cloud rack tiers according to the five QoS metrics. In the third year, primary goals are first, SLA gneration integrating social networks and user context-based multimode sensing,(conducted by subproject 1 and cooperating with this and subprojects 2, and 4), second, network virtualization based on hierarchical and dynamic brokering (conducted by subproject 3 for decision process while subproject 1 provides context, subproject 2 provides computing capability options, subproject 4 provides trusted destinations, third, virtual machines’ hot-spot detection, VM consolidation, and task handoff techniques (conducted by subproject 2, subproject 1 provides context, subproject 3 provides cloud control, subproject 4 handoff destinations), and finally fourth, self-contained AAA for user device groups (conducted by subproject 4 cooperating with subprojects 1, 2, and 3). On the performance fold, in addition to meet SSQ and HCQ, we are enhancing SecQ and accomplishing ICQand EUQ. This project seeks the supports from Center for Research of E-life DIgital Technology (CREDIT) and Advance Multimedia Internet Technology (AMIT) Corporation and we are applying useful invention patents (currently 2 inventions are applied) and targeting real production applications. Thus, in the academic area, industrial applications, and human education we will gain various and solid achievements.[[note]]NSC102-2218-E327-00

DEVICE FOR OPERATION AND CONTROL OF MOTION MODES OF ELECTRICAL EQUIPMENT

[[abstract]]本發明係有關於一種能操控電氣設備作動模式的操控裝置，該影像操控裝置包括一像素感測器及一影像訊號處理器，該影像訊號處理器為一處理影像輸出訊號之積體電路，其包含有一影像梯度計算暨二值化單元、一影像變動偵測單元以及一訊號傳輸單元；其主要係藉由該像素感測器擷取電氣設備操控者的影像後，由該影像訊號處理器進行判斷該操控者的身體動作，進而指示電氣設備執行對應該身體動作之作動模式。[[abstract]]The present invention relates to a device for operation and control of motion modes of electrical equipment, which comprises a pixel sensor and an image signal processor. The image signal processor is an integrated circuit for processing image output and has an image gradient calculation and threshold unit, an image variation detecting unit and a signal transmission unit. After the pixel sensor captures images of an operator controlling an electrical equipment, the image signal processor will determine the operator’s body motions and further instruct the electrical equipment to perform the motion mode relative to the operator’s body motions

教育部補助智慧電子整合性人才培育計畫-100-101年度智慧電子跨領域應用專題系列課程計畫

[[note]]100-E-11-

適應性權重模糊均值影像濾波器之設計與硬體合成方法

[[abstract]]本發明提出一個有效的非線性加權模糊均值濾波器可以濾除影像中高密度污染之高斯突波雜訊(Gaussian impulse noise)。僅藉由單一週期,濾波器的權重便可以根據模糊規則來調整。這些模糊規則是依據影像的歷史統計資料(histogram)所訂定的。在知識庫中所有的模糊規則可同時平行推論以評估輸入訊號的自然特性並且調適濾波器。統計上的分析保證所發明之濾波方法即使在影像已經完全地被污染的情況下依然具有強固性與穩定的效能。本濾波器中的各個重要元件由LR模糊基本元件所合成。該基本元件採用類比電流模式的技術以達到高速、低功率、小電路面積的目標。每一個基本元件都同時地執行每秒20,000,000模糊推論,因此可以使得合成的濾波器每秒至少處理50張256點見方的影像。此實為一高速的濾波器。[[abstract]]This invention proposes a novel nonlinear filter called adaptive weighted fuzzy mean (AWFM) filter which is capable of removing Gaussian and impulse noises in high-density polluted images. Via a single epoch, the weights of the filter are adapted according to a small set of fuzzy rules, which are constructed by a proposed procedure. All the fuzzy rules in the knowledge base infer concurrently for input nature estimation and filter adaptation. Statistic analyses ensure that this AWFM filtering possesses robust and consistent performance even when images are thoroughly polluted. The AWFM filter is synthesized with generic LR fuzzy cells which adopts CMOS analog current mode technology to subject high speed, low power, and small circuit area objectives. Simulations show that parallel inferences realized by the generic LR fuzzy cells, each of which performs 20M FLIPS (fuzzy logic inferences per second) promise that the synthesized AWFM filter is able to process up to 50 256x256 images per second

Cloud Sever Forming Method by Dynamically Clustering User End Devices and System Thereof

[[abstract]]一種動態叢聚使用者端裝置形成雲端伺服器方法及其系統包含：在一預定區域範圍內搜尋至少一第一使用者端裝置；將該第一使用者端裝置選擇為一仲介器裝置；及利用該仲介器裝置形成一第一動態雲端伺服器，且該第一動態雲端伺服器叢聚其它使用者端裝置。在本發明另一較佳實施例中，在該預定區域範圍內再搜尋至少一第二使用者端裝置，且該仲介器裝置選擇於該第一使用者端裝置及第二使用者端裝置之間。[[abstract]]A forming method and system includes: searching at least one first user end device within a predetermined area; selecting the first user end device as a cloud broker device; performing the cloud broker device as a first dynamic cloud server which clusters other user end devices. In another embodiment, further searching at least one second user end device within the predetermined area; selecting the first dynamic cloud server between the first user end device and the second user end device

Self-Organized User Devices as Multi-Clouds with Performance Development Platform

[[abstract]]本計畫採用階層式仲介架構(Hierarchical Brokering Architecture, HiBA)與行動多雲控制 網路框架(Mobile Multicloud Control Network, MMCN)，建構一多雲效能開發平台應用 於民宿觀光旅遊產業，其中階層式仲介架構與行動多雲控制框架來自於過去計畫成 果，在本計畫中特別針對民宿觀光旅遊產業進行規劃，整合共六個子計畫，總計畫兼 子計畫一「自組使用者裝置之多雲運算及其效能開發平台」利用 MMCN 回授框架自 主、自動化、自我組織 HiBA虛擬資源網路架構，提供 IaaS 基礎以及 PaaS、SaaS 框架， 作為其他子計畫開發平台，實作管理面與控制面之各式演算法，並加以統合各演算法 之衝突，與硬體加速器之研究。分離資料面給子計畫五「基於 HiBA 雲端架構的行動 雲端資料處理框架之研究與實作」，專為民宿觀光旅遊產業設計行動分散式資料庫，結 合子計畫四之「行動雲端協同運算安全子系統」保全個資隱私、條件授權、加密簽章、 密鑰傳遞技術，確保子計畫二之「健康風險機率預估子系統」、子計畫三之「線上自動 議價與行程推薦」、與子計畫六之「基於文本探勘技術之社群協同過濾推薦系統」等之 資料以及運算服務可以確保使用者經驗品質，其中子計畫二之「健康風險機率預估子 系統」實作穿衣鏡等相關感測器感測肢體粗大動作，再根據子計畫六之旅遊性質即時 分析提供健康風險評估提供給子計畫三進行線上自動議價與行程推薦。 利用 HiBA 可以動態群組網路資源的特性，各種資訊與資源之分享可以不必上傳公有 雲，即可從其他人的行動或固定設備中取得，透過維持和其他設備之資源連結，可以 持續更新、分析、與過濾。本系統在旅遊前、中、後均提供動態之主動式服務，消費 者出發前由系統自動衡量個人化的身、心、興趣、價格考量，擷取社群網路朋友中對 於身、心、興趣、價格的建議，即時更新社群朋友、業者的多媒體旅遊資訊，包含照 片、影片、文字介紹、導航資訊…等等。除此之外，由於資訊可以即時更新，可以在 旅行中原訂套裝行程之外，再外加其他行程，成為加值行程。旅行後，可以條件授權 變成資訊與資源的提供者，自動分享已經消費完成的套裝行程中，關於身、心、性質、 價格的資訊以及多媒體內容。本計畫系統服務對象具多元性，包含消費者、民宿或旅 行業者、民宿結盟商家、以及社群旁觀者，大幅提昇買、賣、社群成員的經驗品質， 非常利於衍生創新的商業模式。 本計畫預期的產業效益主要鎖定在民宿觀光旅遊之業者，已經和旅行社、旅遊服務行 銷、APP 廠商等進行多次討論，確定此應用方向不但創新，且極具吸引力。本計畫已 和廠商簽訂合作協議書，並已事先和民宿、行銷、雲端 APP 不同業者取得共識，將陸 續簽訂合作計畫，對於學術發展以及跨領域人才培育上均有可期之重大成果。[[abstract]]We exploit Hierarchical Brokering Architecture (HiBA) and Mobile Multicloud Control Network (MMCN) to build a multi-cloud computing environment with performance development platform. We apply this platform in B&B tourism industry. The HiBA and MMCN are enhanced from previous project with enhancement especially on the applications of B&B tourism industry. The whole project comprises six sub-projects. The host and subproject one “Self-organized User Devices as Multi-Clouds with Performance Development Platform” utilizes MMCN to construct autonomous, automatic, and self-roganized HiBA virtual resource network and provisions IaaS infrastructure as well as PaaS and SaaS templates for the other subprojects’ PaaS and SaaS developments. For the IaaS provision, in the subproject one we implement algorithms on the management and control planes with compromises among the algorithms and hardware acceleration. The data plane is separated from the control plane and it is realized by the 5 th subproject “HiBA-based mobile cloud data processing framework” dedicated to the distributed database for B&B tourism industry. Integrating with sub-project four – the “Mobile cloud cooperative computation security subsystem,” the HiBA system includes privacy preservation, conditional authorization, signature encryption, and key distribution techniques to secure the data and operations in subproject two – the “health risk probability prediction subsystem,” subproject three – the “online automatic bargaining and itinerary recommendation subsystem,” and subproject six – the “ontology-based social cooperative filtering and recommendation subsystem.” The subproject two, the “health risk probability prediction subsystem” also implements a dressing mirror embedded with sensors recoding gross motor trajectories for collapse prediction. The results of subproject six performs real-time tourism prefer and travel package products’ intrinsic property analysis. Then, the results of subprojects two and six provide references besides reserved price to subproject three for automatic price negotiation and itinerary recommendation. Exploiting HiBA’s dynamic resource aggregation nature, lots information and resources are instantly sharable from other proximate mobile and static devices without uploading them to public clouds. By maintaining the resource connections among these proximate devices, information and resources are continuously updated, analyzed, and filtered. The whole system provides proactive and complete live services prior to the itinerary, during the itinerary, and after the itinerary. Prior to the itinerary, the system automatically considers a consumer’s body health, mind, prefer, and price status, excerpts evaluations in the social network, and instantly updates friends’ and vendors’ multimedia information comprising photos, movies, plain texts, and GPS navigations. In addition, since the information is instantly updated, during the original planned itinerary, value-added itinerary becomes possible. After the itinerary, conditional authorization enables the consumer becoming a resource and information provider and the consumer can conditionally authorize publication of the health, prefer, price, and multimedia information experienced during the itinerary with privacy. The users of our system are diverse including consumers, tourism products vendors, B&Bs, heterogeneous industries in alliance the B&Bs, and onlookers in the social network. Our system enormously promotes the quality of experience (QoE) such that it is beneficial to create innovative business model. The project expected benefits are focus on the B&B tourism industry. We have already consulted with travel agencies, tourism marketing people, and APP vendors through several discussions and confirm that the system is innovative and attractive. With cooperation memos and common consensus with them, we are now seeking further cooperated programs. In addition, the enormous academic development and cultivation of multi-discipline talents are both expectable.[[note]]MOST103-2221-E327-04