To prepare your seminar report based on the provided PDF file on "Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes," I will create a detailed and comprehensive three-slide presentation covering the "Background Introduction," "Research Motivation," and "Research Questions" sections. Below is an outline and detailed explanation for each slide, incorporating relevant information from the document:為了根據提供的關於「基於V2I 和V2V 模式的車輛邊緣計算的聯合任務卸載和資源分配」的PDF 文件準備研討會報告，我將創建一個詳細而全面的三張幻燈片演示文稿，涵蓋「背景介紹」、「研究動機」和「研究問題」部分。以下是每張投影片的概述和詳細說明，其中包含文件中的相關資訊：
Slide 1: Background Introduction投影片 1：背景介紹
Title: Enhancing Vehicular Networks through Edge Computing標題：透過邊緣運算增強車輛網絡
Content:內容：
Overview of Vehicular Edge Computing (VEC):車載邊緣運算 (VEC) 概述：
- VEC harnesses the computational capabilities of both vehicles and roadside units (RSUs) to process tasks more efficiently within vehicular networks.VEC 利用車輛和路邊單元 (RSU) 的運算能力來更有效地處理車輛網路中的任務。
- It contrasts with traditional cloud computing by bringing processing power closer to where data is generated, significantly reducing latency.與傳統雲端運算相比，它使處理能力更接近資料生成的地方，從而顯著減少延遲。
V2I and V2V Communication Technologies:V2I和V2V通訊技術：
- Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) are critical wireless communication technologies that underpin VEC.車對基礎設施 (V2I) 和車對車 (V2V) 是支援 VEC 的關鍵無線通訊技術。
- V2I involves vehicles connecting to fixed infrastructure like RSUs, while V2V allows vehicles to communicate directly with each other.V2I 涉及車輛連接到 RSU 等固定基礎設施，而 V2V 允許車輛直接相互通訊。
Role of the Cellular Network:蜂窩網路的作用：
- Manages the spectrum and ensures seamless connectivity between different components of the vehicular network.管理頻譜並確保車輛網路不同組件之間的無縫連接。
Visuals: Diagram illustrating VEC, showing vehicles, RSUs, and connections (V2I and V2V).視覺效果：說明 VEC 的圖表，顯示車輛、RSU 和連接（V2I 和 V2V）。
Slide 2: Research Motivation投影片 2：研究動機
Title: Addressing the Challenges in Vehicular Networks標題：應對車載網路的挑戰
Content:內容：
Limitations of Current VEC Systems:目前 VEC 系統的限制：
- Many existing VEC systems only utilize V2I or V2V exclusively, not leveraging the potential benefits of integrating both modes.許多現有的 VEC 系統僅使用 V2I 或 V2V，而沒有利用整合這兩種模式的潛在優勢。
- Current approaches may not efficiently manage the network’s computational and channel resources, leading to potential overloads and imbalanced load distribution.目前的方法可能無法有效管理網路的運算和通道資源，從而導致潛在的過載和負載分佈不平衡。
Need for Enhanced Task Offloading Strategies:需要增強的任務卸載策略：
- There is a crucial need for dynamic and flexible task offloading strategies that can adapt to various real-time conditions and requirements of the vehicular environment.迫切需要動態且靈活的任務卸載策略，以適應車輛環境的各種即時條件和要求。
- Such strategies would ideally minimize task processing delays and optimize resource utilization across both RSUs and vehicles.理想情況下，此類策略將最大限度地減少任務處理延遲並優化 RSU 和車輛的資源利用率。
Visuals: Graphs showing latency issues and resource allocation inefficiencies in existing systems.視覺效果：顯示現有系統中的延遲問題和資源分配效率低下的圖表。
Slide 3: Research Questions投影片 3：研究問題
Title: Key Questions Driving Future Innovations標題：推動未來創新的關鍵議題
Content:內容：
How can VEC systems integrate both V2I and V2V to enhance overall performance?VEC系統如何整合V2I和V2V以提高整體效能？
- Investigating methods for seamless integration of these technologies to support robust, scalable, and efficient vehicular networks.研究無縫整合這些技術的方法，以支援穩健、可擴展且高效的車輛網路。
What are the optimal strategies for task offloading and resource allocation in mixed V2I/V2V environments?在混合 V2I/V2V 環境中任務卸載和資源分配的最佳策略是什麼？
- Developing algorithms that can dynamically allocate tasks and resources based on real-time network conditions and vehicle states.開發能夠根據即時網路狀況和車輛狀態動態分配任務和資源的演算法。
Can we develop a universal framework that addresses the diverse needs of different vehicular network scenarios?我們能否發展一個通用的框架來滿足不同車聯網場景的多元需求？
- Proposing a flexible framework capable of adapting to various vehicular contexts and requirements, including urban, rural, and highway scenarios.提出一個能夠適應各種車輛環境和要求的靈活框架，包括城市、鄉村和高速公路場景。
Visuals: Question marks, icons representing different network scenarios, and conceptual diagrams of proposed frameworks.視覺效果：問號、代表不同網路場景的圖示、建議框架的概念圖。
Each slide will be structured to provide a clear, logical flow of information that builds on the previous content, ensuring that the audience gains a comprehensive understanding of the topic without needing to refer to the original document. This approach addresses the core aspects of your seminar report, making it accessible and informative.每張投影片的結構都將提供基於先前內容的清晰、邏輯的資訊流，確保觀眾無需參考原始文件即可全面了解該主題。這種方法解決了研討會報告的核心方面，使其易於理解且內容豐富。
For your seminar presentation based on the provided PDF file on "Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes," here is a detailed description of each slide that you can include in your memo. This description will ensure that each slide accurately reflects the content of the article, focusing on key aspects such as the background introduction, research motivation, and research questions.對於基於所提供的 PDF 文件「基於 V2I 和 V2V 模式的車輛邊緣計算的聯合任務卸載和資源分配」的研討會演示，這裡是您可以在備忘錄中包含的每張幻燈片的詳細說明。這種描述將確保每張投影片準確反映文章的內容，重點關注背景介紹、研究動機和研究問題等關鍵方面。
Slide 1: Background Introduction投影片 1：背景介紹
Title: Enhancing Vehicular Networks through Edge Computing標題：透過邊緣運算增強車輛網絡
Description: This slide introduces the concept of Vehicular Edge Computing (VEC), emphasizing its significance in improving vehicular network efficiency by utilizing the computational capabilities of vehicles and roadside units (RSUs). It contrasts traditional cloud computing approaches by bringing computation closer to data sources, thereby reducing latency. The slide also explains the critical technologies that enable VEC: Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. These are framed within the context of their management and optimization by the cellular network, which ensures robust connectivity and efficient performance across the vehicular network.描述： 本投影片介紹了車輛邊緣運算（VEC）的概念，強調了其利用車輛和路邊單元（RSU）的運算能力來提高車輛網路效率的重要性。它與傳統的雲端運算方法形成對比，使運算更接近資料來源，從而減少延遲。該幻燈片還解釋了實現 VEC 的關鍵技術：車輛到基礎設施 (V2I) 和車輛到車輛 (V2V) 通訊。這些都是在蜂窩網路的管理和優化背景下建造的，這確保了整個車輛網路的穩健連接和高效性能。
Visuals: Include a diagram that illustrates the VEC framework, showing how vehicles and RSUs interact through V2I and V2V connections. This visual will help clarify the network structure for the audience.視覺效果： 包含一張說明 VEC 框架的圖表，顯示車輛和 RSU 如何透過 V2I 和 V2V 連線進行互動。此視覺效果將有助於向觀眾闡明網路結構。
Slide 2: Research Motivation投影片 2：研究動機
Title: Addressing the Challenges in Vehicular Networks標題：應對車載網路的挑戰
Description: This slide delves into the current limitations and challenges faced by VEC systems, particularly focusing on the inefficiencies in existing task offloading and resource allocation strategies. It highlights the problem of existing systems predominantly utilizing either V2I or V2V modes but not both, which can lead to resource overloads and inefficient task distribution. The slide argues for the development of dynamic and flexible task offloading strategies that can effectively balance load and minimize processing delays, optimizing both computational and channel resources across the vehicular network.描述： 本投影片深入探討了 VEC 系統目前面臨的限制和挑戰，特別關注現有任務卸載和資源分配策略的低效率。它強調了現有系統主要使用 V2I 或 V2V 模式而不是同時使用兩種模式的問題，這可能導致資源過載和任務分配效率低下。該投影片主張開發動態且靈活的任務卸載策略，可以有效平衡負載並最大限度地減少處理延遲，從而優化整個車輛網路的運算和通道資源。
Visuals: Graphs displaying the latency and resource allocation issues in current VEC implementations will be effective in illustrating the need for improved strategies.視覺效果： 顯示目前 VEC 實施中的延遲和資源分配問題的圖表將有效說明改善策略的必要性。
Slide 3: Research Questions投影片 3：研究問題
Title: Key Questions Driving Future Innovations標題：推動未來創新的關鍵議題
Description: The final slide outlines the fundamental research questions that guide the ongoing development and innovation within VEC systems. It poses critical inquiries about how integrated V2I and V2V systems can enhance vehicular network performance, the development of optimal task offloading and resource allocation strategies in mixed V2I/V2V environments, and the potential for a universal framework capable of adapting to diverse vehicular scenarios. These questions aim to spur discussion on creating more adaptable, robust, and efficient vehicular networks.描述： 最後一張投影片概述了指導 VEC 系統內持續開發和創新的基礎研究問題。它對整合的 V2I 和 V2V 系統如何增強車輛網路性能、在混合 V2I/V2V 環境中製定最佳任務卸載和資源分配策略以及能夠適應不同車輛場景的通用框架的潛力提出了重要的疑問。這些問題旨在激發關於創建適應性更強、更強大、更有效率的車輛網路的討論。
Visuals: Use question marks and icons representing various network scenarios (urban, rural, highway) alongside conceptual diagrams of potential frameworks. These visuals should emphasize the scope of applicability and the innovative aspects of proposed solutions.視覺效果： 使用代表各種網路場景（城市、鄉村、高速公路）的問號和圖標以及潛在框架的概念圖。這些視覺效果應強調所提出解決方案的適用範圍和創新方面。
This structured presentation not only reflects a thorough understanding of the provided article but also strategically organizes the information to enhance clarity and engagement for the audience. The detailed descriptions and visuals are designed to facilitate an easy grasp of complex concepts, encouraging a deeper discussion during your seminar.這種結構化的演示不僅反映了對所提供文章的透徹理解，而且還策略性地組織訊息以提高觀眾的清晰度和參與度。詳細的描述和視覺效果旨在幫助您輕鬆掌握複雜的概念，鼓勵您在研討會期間進行更深入的討論。
Based on the information gathered from the PDF, here's how you can create a presentation with 2 slides that cover "Literature Review," "Existing Related Technologies," and "Existing Related Research Results" from the document on Vehicular Edge Computing (VEC):根據從PDF 中收集的信息，您可以按照以下步驟建立包含2 張幻燈片的演示文稿，其中涵蓋車輛邊緣計算(VEC) 文件中的「文獻綜述」、「現有相關技術」和「現有相關研究結果”：
Slide 1: Literature Review and Existing Related Technologies投影片 1：文獻綜述與現有相關技術
Title: Evolution of Vehicular Edge Computing Technologies標題：車輛邊緣運算技術的演變
Description: This slide reviews existing technologies and literature that have shaped the development of Vehicular Edge Computing (VEC), especially in scenarios involving Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications.描述： 本幻燈片回顧了影響車輛邊緣運算 (VEC) 發展的現有技術和文獻，特別是在涉及車輛到基礎設施 (V2I) 和車輛到車輛 (V2V) 通訊的場景中。
Content:內容：
IoV and VEC Overview:IoV 和 VEC 概述：
- Internet of Vehicles (IoV) serves as a platform for vehicles to interact and share information, leveraging technologies like V2I and V2V .車聯網 (IoV) 是車輛利用 V2I 和 V2V 等技術進行互動和分享資訊的平台。
- VEC utilizes computing resources from both vehicles and roadside units (RSUs) to enhance task processing capabilities, emphasizing the importance of effective task offloading and resource allocation strategies .VEC利用車輛和路邊單元（RSU）的運算資源來增強任務處理能力，強調有效的任務卸載和資源分配策略的重要性。
Existing Technologies:現有技術：
- The current landscape includes methods where vehicles offload tasks to RSUs (V2I) or to other vehicles (V2V). However, these methods often do not utilize the full potential of both technologies simultaneously, leading to inefficiencies in resource use and increased latency .目前的情況包括車輛將任務卸載給 RSU (V2I) 或其他車輛 (V2V) 的方法。然而，這些方法通常無法同時充分利用兩種技術的潛力，導致資源使用效率低並增加延遲。
Visuals:視覺效果：
- Diagrams illustrating V2I and V2V communication paths.說明 V2I 和 V2V 通訊路徑的圖表。
- Table comparing different task offloading strategies and their effectiveness in various IoV scenarios.表格比較了不同的任務卸載策略及其在各種 IoV 場景中的有效性。
Slide 2: Existing Related Research Results投影片 2：現有相關研究成果
Title: Advancements and Limitations in VEC標題：VEC 的進步與局限性
Description: This slide delves into the specific studies and results that have contributed to our understanding of VEC, highlighting both the progress made and the gaps that still exist.描述： 這張投影片深入探討了有助於我們理解 VEC 的具體研究和結果，強調了所取得的進展和仍然存在的差距。
Content:內容：
Recent Studies:最近的研究：
- Research has explored dynamic task offloading and resource allocation, with some studies utilizing machine learning algorithms to optimize these processes. However, challenges remain in channel allocation and the seamless integration of V2I and V2V modes .研究探討了動態任務卸載和資源分配，其中一些研究利用機器學習演算法來優化這些過程。但通路分配以及V2I和V2V模式的無縫整合仍面臨挑戰。
Critical Gaps and Future Directions:關鍵差距與未來方向：
- Despite advancements, existing studies often overlook the potential of combining V2I and V2V offloading in a unified framework. There is a significant opportunity to develop more flexible and efficient systems that better manage the computational and channel resources in real-time vehicular environments .儘管取得了進步，但現有的研究往往忽略了在統一框架中結合 V2I 和 V2V 卸載的潛力。這是開發更靈活、更有效率的系統的重要機會，可以更好地管理即時車輛環境中的運算和通道資源。
Visuals:視覺效果：
- Charts displaying the outcomes of different optimization algorithms used in recent studies.顯示最近研究中使用的不同最佳化演算法的結果的圖表。
- A roadmap graphic outlining future research directions and potential improvements in VEC technologies.路線圖概述了 VEC 技術的未來研究方向和潛在改進。
Summary: This presentation aims to provide a coherent and comprehensive overview of the literature and technologies related to VEC, outlining both the advancements made and the avenues for future research. By examining existing methodologies and identifying key research gaps, the seminar can facilitate a deeper understanding of how VEC can be optimized for better performance across various vehicular networks.概括： 本演講旨在提供與 VEC 相關的文獻和技術的連貫且全面的概述，概述所取得的進展和未來研究的途徑。透過檢查現有方法並確定關鍵研究差距，研討會可以促進更深入地了解如何優化 VEC 以在各種車輛網路中獲得更好的性能。
Based on the provided PDF document titled "Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes," here is a detailed presentation structure with three slides focusing on "Data Collection," "Network Topology Design," and "Task Experiment Design." This structure aims to explain the key points clearly and coherently for a seminar audience.基於所提供的標題為“基於V2I 和V2V 模式的車輛邊緣計算的聯合任務卸載和資源分配”的PDF 文檔，這裡是詳細的演示結構，其中三張幻燈片重點關注“數據收集”、“網絡拓撲設計”和“任務實驗設計。這種結構旨在為研討會觀眾清晰、連貫地解釋要點。
Slide 1: Data Collection投影片 1：數據收集
Title: Data Collection Strategies in VEC Systems標題：VEC 系統中的資料收集策略
Content:內容：
Overview of Data Collection in VEC:VEC 中的資料收集概述：
- Data collection is crucial for optimizing task offloading and resource allocation in Vehicular Edge Computing (VEC).資料收集對於優化車輛邊緣運算 (VEC) 中的任務卸載和資源分配至關重要。
- The system relies on real-time data from vehicles and infrastructure to make informed decisions about task offloading .該系統依靠來自車輛和基礎設施的即時數據來做出有關任務卸載的明智決策。
Methods of Data Acquisition:數據採集方法：
- Data is primarily collected from two sources: vehicle telemetry (V2V) and infrastructure sensors (V2I).數據主要從兩個來源收集：車輛遙測 (V2V) 和基礎設施感測器 (V2I)。
- The collected data includes vehicle speed, task data size, and available computational resources .收集的數據包括車輛速度、任務資料大小和可用的運算資源。
Visuals:視覺效果：
- Graphical representation of data flow from vehicles and RSUs to the edge server.從車輛和 RSU 到邊緣伺服器的資料流的圖形表示。
- Icons representing various data types (e.g., speed, computational capacity).代表各種資料類型（例如速度、運算能力）的圖示。
Slide 2: Network Topology Design投影片 2：網路拓樸設計
Title: Designing Network Topology for VEC標題：設計 VEC 網路拓撲
Content:內容：
Topology Overview:拓樸概述：
- The network topology involves multiple vehicles connected through a Road Side Unit (RSU) equipped with an edge server.網路拓撲涉及透過配備邊緣伺服器的路邊單元（RSU）連接的多輛車輛。
- The RSU’s coverage is modeled as a circular area, optimizing the connectivity and data transmission across vehiclesRSU 的覆蓋範圍被建模為圓形區域，優化了車輛之間的連接和資料傳輸.。
Vehicle Classification and Task Distribution:車輛分類及任務分配：
- Vehicles are classified based on their task requirements and willingness to provide computational services.車輛根據其任務要求和提供計算服務的意願進行分類。
- Task allocation and offloading decisions are made based on vehicle types and their current network positions任務分配和卸載決策是根據車輛類型及其當前網路位置做出的.。
Visuals:視覺效果：
- Diagram of the network topology showing RSU coverage and vehicle communication links.顯示 RSU 覆蓋範圍和車輛通訊鏈路的網路拓撲圖。
- Table categorizing vehicles into groups based on their task handling capabilities.根據車輛的任務處理能力將車輛分組的表格。
Slide 3: Task Experiment Design投影片 3：任務實驗設計
Title: Experimental Setup for Task Offloading標題：任務卸載的實驗設置
Content:內容：
Experiment Objectives:實驗目的：
- To evaluate the efficiency of task offloading strategies and resource allocation within a simulated urban V2I and V2V environment.評估模擬城市 V2I 和 V2V 環境中任務卸載策略和資源分配的效率。
- Focus on reducing the total task processing delay through optimized offloading專注於透過優化卸載來減少總任務處理延遲.。
Simulation Parameters:模擬參數：
- Using MATLAB simulations, parameters such as vehicle speed, task size, and computing resources are varied to assess performance impacts.使用 MATLAB 仿真，改變車速、任務大小和計算資源等參數來評估效能影響。
- The models consider different scenarios to predict system behavior under various operational conditions這些模型考慮不同的場景來預測各種操作條件下的系統行為.。
Visuals:視覺效果：
- Flowchart of the experimental process, showing steps from data collection to task execution.實驗過程的流程圖，顯示了從資料收集到任務執行的步驟。
- Charts showing simulation results with different parameter settings.顯示不同參數設定下的模擬結果的圖表。
Summary: This presentation provides a detailed exploration of the data collection methods, network topology, and task experimentation in VEC systems. Each slide builds on the foundational knowledge from the PDF to present a clear, comprehensive understanding of how VEC systems are designed and tested to optimize task offloading and resource allocation effectively.概括： 本示範詳細探討了 VEC 系統中的資料收集方法、網路拓撲和任務實驗。每張投影片均以 PDF 中的基礎知識為基礎，清晰、全面地了解 VEC 系統的設計和測試方式，以有效優化任務卸載和資源分配。
For your seminar presentation based on the provided PDF file on "Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes," here are detailed descriptions of each slide to include in your memo. These descriptions will ensure that each slide comprehensively reflects the content of the article, focusing on "Data Collection," "Network Topology Design," and "Task Experiment Design."對於基於所提供的 PDF 文件「基於 V2I 和 V2V 模式的車輛邊緣計算的聯合任務卸載和資源分配」的研討會演示，以下是要包含在備忘錄中的每張幻燈片的詳細說明。這些描述將確保每張投影片全面反映文章的內容，重點是「資料收集」、「網路拓撲設計」和「任務實驗設計」。
Slide 1: Data Collection投影片 1：數據收集
Title: Data Collection Strategies in VEC Systems標題：VEC 系統中的資料收集策略
Description: This slide provides an overview of the data collection mechanisms critical to the optimization of task offloading and resource allocation within Vehicular Edge Computing (VEC). It details the process of gathering real-time data from both vehicles (V2V) and infrastructure sensors (V2I), which is essential for making informed decisions about task offloading. The slide emphasizes the types of data collected, including vehicle speed, task data size, and available computational resources, all of which are crucial for the dynamic management of VEC systems.描述： 本投影片概述了對於車輛邊緣運算 (VEC) 內的任務卸載和資源分配最佳化至關重要的資料收集機制。它詳細介紹了從車輛 (V2V) 和基礎設施感測器 (V2I) 收集即時數據的過程，這對於做出有關任務卸載的明智決策至關重要。這張投影片強調了收集的資料類型，包括車輛速度、任務資料大小和可用運算資源，所有這些對於 VEC 系統的動態管理至關重要。
Visuals:視覺效果：
- A diagram illustrating the data flow from vehicles and Road Side Units (RSUs) to the edge server, highlighting the interaction between V2I and V2V communications.該圖展示了從車輛和路邊單元 (RSU) 到邊緣伺服器的資料流，突出顯示了 V2I 和 V2V 通訊之間的交互作用。
- Icons and symbols representing different types of data such as speed, data size, and computing capacity, helping to visualize the diversity of information processed.代表不同類型資料的圖示和符號，例如速度、資料大小和運算能力，有助於視覺化處理資訊的多樣性。
Slide 2: Network Topology Design投影片 2：網路拓樸設計
Title: Designing Network Topology for VEC標題：設計 VEC 網路拓撲
Description: This slide explores the network topology design that underpins effective VEC systems, focusing on the connectivity facilitated by RSUs equipped with edge servers. It describes how the RSU’s coverage is modeled as a circular area, optimizing connectivity and data transmission across vehicles within the network. Additionally, the slide explains the classification of vehicles based on their task requirements and computational service capabilities, which are key factors in determining task allocation and offloading decisions.描述： 本投影片探討了支援有效 VEC 系統的網路拓撲設計，重點在於配備邊緣伺服器的 RSU 所促進的連線。它描述瞭如何將 RSU 的覆蓋範圍建模為圓形區域，從而優化網路內車輛之間的連接和資料傳輸。此外，該投影片還解釋了根據任務要求和計算服務能力對車輛進行分類，這是確定任務分配和卸載決策的關鍵因素。
Visuals:視覺效果：
- A detailed diagram of the network topology, showing the RSU's coverage area and the communication links between vehicles.網路拓撲的詳細圖，顯示 RSU 的覆蓋範圍和車輛之間的通訊鏈路。
- A table categorizing vehicles into different groups according to their task handling and computational service capacities, aiding in the understanding of network dynamics.根據任務處理和運算服務能力將車輛分為不同組別的表格，有助於理解網路動態。
Slide 3: Task Experiment Design投影片 3：任務實驗設計
Title: Experimental Setup for Task Offloading標題：任務卸載的實驗設置
Description: The final slide delves into the experimental design used to evaluate task offloading strategies and resource allocation within a simulated urban V2I and V2V environment. It outlines the objectives of reducing total task processing delays through optimized offloading strategies. The slide further discusses the simulation parameters used in MATLAB, such as varying vehicle speeds, task sizes, and computing resources, to assess the performance impacts under different operational conditions.描述： 最後一張投影片深入研究了用於評估模擬城市 V2I 和 V2V 環境中的任務卸載策略和資源分配的實驗設計。它概述了透過優化卸載策略來減少總任務處理延遲的目標。這張投影片進一步討論了 MATLAB 中使用的模擬參數，例如不同的車輛速度、任務大小和計算資源，以評估不同操作條件下的性能影響。
Visuals:視覺效果：
- A flowchart depicting the experimental process from data collection to task execution, providing a step-by-step breakdown of the methodology.流程圖描述了從資料收集到任務執行的實驗過程，提供了該方法的逐步分解。
- Charts and graphs presenting the results of simulations under various parameter settings, illustrating the impacts on system performance.圖表和圖形顯示了各種參數設定下的模擬結果，說明了對系統性能的影響。
Summary: This presentation is designed to provide a comprehensive understanding of the intricate details of VEC systems as outlined in the provided PDF. Each slide builds on the foundational concepts to present a clear, detailed picture of how data is collected, how network topology is designed, and how task offloading experiments are conducted, ensuring that seminar attendees can grasp the key points without needing to read the full document.概括： 本示範旨在讓您全面了解所提供的 PDF 中概述的 VEC 系統的複雜細節。每張投影片都建立在基本概念的基礎上，清晰、詳細地介紹瞭如何收集資料、如何設計網路拓撲以及如何進行任務卸載實驗，確保研討會與會者無需閱讀完整文件即可掌握要點。
Here is a suggested structure for a presentation containing four slides, based on the PDF titled "Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes." The slides will focus on "System Model Design" and "Algorithm Design," providing a detailed and understandable summary of these complex topics.以下是包含四張投影片的簡報的建議結構，基於標題為「基於 V2I 和 V2V 模式的車輛邊緣運算的聯合任務卸載和資源分配」的 PDF。這些投影片將重點關注“系統模型設計”和“演算法設計”，對這些複雜主題提供詳細且易於理解的總結。
Slide 1: Introduction to System Model Design投影片 1：系統模型設計簡介
Title: Framework of System Model in VEC標題：VEC系統模型框架
Content:內容：
- Overview of the System Model:系統模型概述：
- Introduction to the topology involving multiple vehicles and a Road Side Unit (RSU), facilitating the understanding of vehicular network interactions .介紹涉及多輛車輛和路邊單元 (RSU) 的拓撲，有助於理解車輛網路互動。
- Description of task offloading processes including task models, communication models, and computing models, which are crucial for setting up the system model .任務卸載過程的描述，包括任務模型、通訊模型和計算模型，這對於建立系統模型至關重要。
Visuals:視覺效果：
- Diagram of the network topology with RSU and vehicle interactions.RSU 和車輛互動的網路拓撲圖。
- Icons representing different aspects of the system model (task, communication, computing).代表系統模型不同面向（任務、通訊、計算）的圖示。
Slide 2: Network Topology and Assumptions投影片 2：網路拓樸與假設
Title: Detailed Network Topology and Model Assumptions標題：詳細的網路拓撲和模型假設
Content:內容：
Network Topology Details:網路拓撲詳細資訊：
- Discussion on the circular coverage of RSUs and the bidirectional expressway settings .路側單元循環覆蓋及雙向高速公路設置探討。
- Explanation of vehicle classification based on task offloading needs and service provision .根據任務卸載需求和服務提供車輛分類的解釋。
Model Assumptions:模型假設：
- Key assumptions like flat channels for each vehicle to simplify the model and highlight realistic vehicular communication scenarios .諸如每輛車的平坦通道之類的關鍵假設可以簡化模型並突出現實的車輛通訊場景。
Visuals:視覺效果：
- Detailed diagrams showing RSU coverage and vehicle movement directions.顯示 RSU 覆蓋範圍和車輛移動方向的詳細圖表。
- Bullet points listing the assumptions with icons representing their implications on the model.列出假設的要點，並用圖示表示它們對模型的影響。
Slide 3: Introduction to Algorithm Design投影片 3：演算法設計簡介
Title: Optimizing VEC with Advanced Algorithms標題：使用進階演算法優化 VEC
Content:內容：
- Optimal and Heuristic Algorithms:最優和啟發式演算法：
- Introduction to the optimal joint task offloading and resource allocation algorithm (OJTR) using methods like Generalized Benders Decomposition (GBD) and Reformulation Linearization (RL) .介紹使用廣義 Benders 分解 (GBD) 和重構線性化 (RL) 等方法的最佳聯合任務卸載和資源分配演算法 (OJTR)。
- Discussion on the heuristic algorithm for providing sub-optimal solutions with reduced computational complexity .討論提供次優解決方案並降低計算複雜度的啟發式演算法。
Visuals:視覺效果：
- Flowcharts illustrating the steps of OJTR and heuristic algorithms.說明 OJTR 和啟發式演算法步驟的流程圖。
- Comparative table showing the differences and benefits of each algorithm.顯示每種演算法的差異和優點的比較表。
Slide 4: Algorithm Complexity and Performance投影片 4：演算法複雜性與效能
Title: Algorithm Performance and System Optimization標題：演算法效能與系統最佳化
Content:內容：
Complexity Analysis:複雜度分析：
- Analysis of the time complexity of the OJTR algorithm, emphasizing the conditions under which it performs optimally .分析OJTR演算法的時間複雜度，強調其效能最優的條件。
- Insights into the trade-offs between computational complexity and performance optimization .深入了解計算複雜性和效能最佳化之間的權衡。
Performance Metrics:性能指標：
- Overview of system performance improvements and optimization challenges addressed by the algorithms .概述演算法所解決的系統效能改進和最佳化挑戰。
Visuals:視覺效果：
- Graphs showing performance metrics and complexity analysis results.顯示效能指標和複雜度分析結果的圖表。
- Icons highlighting key performance indicators and optimization benefits.突出顯示關鍵效能指標和優化優勢的圖示。
Summary: This presentation is designed to systematically unfold the complexities of the system model and algorithm designs within Vehicular Edge Computing as detailed in the provided PDF. Each slide builds upon the previous to provide a comprehensive understanding of how these elements interact to enhance system performance, making the content accessible and engaging for seminar attendees.概括： 本示範旨在系統地展示車輛邊緣運算中系統模型和演算法設計的複雜性，如所提供的 PDF 中詳細介紹的。每張投影片都建立在上一張投影片的基礎上，全面了解這些元素如何相互作用以增強系統效能，使研討會與會者能夠輕鬆存取和參與內容。
For your seminar presentation based on the provided PDF file on "Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes," here are detailed descriptions for each slide that you can include in your memo. These descriptions ensure that each slide effectively communicates the complexities and essential aspects of system model design and algorithm design, as outlined in the PDF.對於基於所提供的 PDF 文件「基於 V2I 和 V2V 模式的車輛邊緣計算的聯合任務卸載和資源分配」的研討會演示，以下是您可以在備忘錄中包含的每張幻燈片的詳細說明。這些描述可確保每張投影片有效傳達系統模型設計和演算法設計的複雜性和基本面，如 PDF 所述。
Slide 1: Introduction to System Model Design投影片 1：系統模型設計簡介
Title: Framework of System Model in VEC標題：VEC系統模型框架
Description: This slide introduces the foundational framework of the system model utilized in Vehicular Edge Computing (VEC). It details the network topology comprising multiple vehicles connected to a Road Side Unit (RSU), crucial for understanding the dynamics of vehicular networks. The slide further elaborates on the components of the system model, including the task, communication, and computing models, which collectively facilitate the effective implementation and management of task offloading processes in VEC systems.描述： 本幻燈片介紹了車輛邊緣運算 (VEC) 中使用的系統模型的基本框架。它詳細介紹了由連接到路邊單元 (RSU) 的多輛車輛組成的網路拓撲，這對於理解車輛網路的動態至關重要。這張投影片進一步詳細闡述了系統模型的組成部分，包括任務、通訊和計算模型，它們共同促進了 VEC 系統中任務卸載過程的有效實施和管理。
Visuals:視覺效果：
- A comprehensive diagram illustrates the network topology, showing interactions between the RSU and vehicles.綜合圖表說明了網路拓撲，顯示了 RSU 和車輛之間的交互作用。
- Distinct icons represent different components of the system model, such as tasks, communications, and computations, aiding in the visual differentiation and understanding of these elements.不同的圖示代表系統模型的不同組件，例如任務、通訊和計算，有助於視覺區分和理解這些元素。
Slide 2: Network Topology and Assumptions投影片 2：網路拓樸與假設
Title: Detailed Network Topology and Model Assumptions標題：詳細的網路拓撲和模型假設
Description: This slide dives deeper into the specifics of the network topology and the underlying assumptions that support the system model. It describes the circular coverage area of the RSU and its interaction with vehicles on a bidirectional expressway, crucial for task offloading and resource allocation. Additionally, the slide discusses the classification of vehicles based on their roles in task offloading and service provision, which is pivotal for optimizing network operations. It also highlights the model assumptions, such as the consideration of flat channels for each vehicle, which simplifies the model while maintaining a realistic approach to vehicular communications.描述： 本投影片深入探討了網路拓撲的細節以及支援系統模型的基本假設。它描述了 RSU 的圓形覆蓋區域及其與雙向高速公路上車輛的交互，這對於任務卸載和資源分配至關重要。此外，幻燈片還討論了根據車輛在任務卸載和服務提供中的作用對車輛進行分類，這對於優化網路運營至關重要。它還強調了模型假設，例如考慮每輛車的平坦通道，這簡化了模型，同時保持了車輛通訊的現實方法。
Visuals:視覺效果：
- Detailed topology diagrams show the RSU's coverage area and the directions of vehicle movement.詳細的拓撲圖顯示了 RSU 的覆蓋範圍和車輛移動的方向。
- A list of model assumptions is presented with corresponding icons that help illustrate the practical implications of these assumptions on the model.模型假設清單帶有相應的圖標，有助於說明這些假設對模型的實際影響。
Slide 3: Introduction to Algorithm Design投影片 3：演算法設計簡介
Title: Optimizing VEC with Advanced Algorithms標題：使用進階演算法優化 VEC
Description: The third slide introduces the algorithms designed to optimize task offloading and resource allocation within the VEC framework. It explains the Optimal Joint Task Offloading and Resource Allocation (OJTR) algorithm, which utilizes advanced techniques such as Generalized Benders Decomposition (GBD) and Reformulation Linearization (RL) for optimal solutions. The slide also covers the heuristic algorithm that offers sub-optimal solutions with reduced computational complexity, providing a practical alternative for scenarios where computational resources or time are constraints.描述： 第三張投影片介紹了旨在優化 VEC 框架內的任務卸載和資源分配的演算法。它解釋了最佳聯合任務卸載和資源分配 (OJTR) 演算法，該演算法利用廣義 Benders 分解 (GBD) 和重構線性化 (RL) 等先進技術來獲得最佳解決方案。這張投影片還介紹了啟發式演算法，該演算法提供了計算複雜性較低的次優解決方案，為計算資源或時間有限的場景提供了實用的替代方案。
Visuals:視覺效果：
- Flowcharts detail the step-by-step processes of both the OJTR and heuristic algorithms.流程圖詳細介紹了 OJTR 和啟發式演算法的逐步過程。
- A comparative table highlights the differences in approach and benefits between the optimal and heuristic algorithms, aiding in understanding their respective applications and advantages.比較表突顯了最佳演算法和啟發式演算法在方法和優勢方面的差異，有助於理解它們各自的應用和優勢。
Slide 4: Algorithm Complexity and Performance投影片 4：演算法複雜性與效能
Title: Algorithm Performance and System Optimization標題：演算法效能與系統最佳化
Description: This final slide analyzes the complexity and performance of the algorithms discussed in the previous slide. It provides a detailed look at the time complexity of the OJTR algorithm and discusses the conditions under which it achieves optimal performance. The slide also addresses the trade-offs involved in balancing computational complexity against performance optimization. Additionally, it outlines the key performance improvements and optimization challenges that the algorithms are designed to address, reflecting their impact on the overall system efficiency.描述： 最後一張投影片分析了上一張投影片中討論的演算法的複雜性和效能。它詳細介紹了 OJTR 演算法的時間複雜度，並討論了其實現最佳性能的條件。該幻燈片還討論了平衡計算複雜度與效能最佳化所涉及的權衡。此外，它還概述了演算法旨在解決的關鍵性能改進和最佳化挑戰，反映了它們對整體系統效率的影響。
Visuals:視覺效果：
- Performance metrics and complexity analysis results are depicted in graphs, illustrating the effectiveness of the algorithms under various conditions.效能指標和複雜性分析結果以圖表形式示出，說明了演算法在各種條件下的有效性。
- Key performance indicators and benefits of optimization are highlighted through icons, helping to summarize the advantages of implementing these algorithms.透過圖示突顯關鍵效能指標和最佳化的好處，有助於總結實施這些演算法的優勢。
Summary: Each slide in this presentation is carefully crafted to explain the complex aspects of system model and algorithm design within VEC, ensuring that the content is both accessible and engaging for the seminar audience. The detailed descriptions and visuals are tailored to facilitate a clear understanding of how each component of the system model and algorithm contributes to enhancing the efficiency and performance of VEC systems.概括： 本簡報中的每張投影片都經過精心製作，旨在解釋 VEC 內系統模型和演算法設計的複雜方面，確保研討會觀眾可以理解並吸引內容。詳細的描述和視覺效果經過定制，有助於清楚地了解系統模型和演算法的每個組件如何有助於提高 VEC 系統的效率和性能。
Based on the information from the PDF "Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes," here is a presentation structure with three slides focusing on "Simulation Results" and "Performance Evaluation":基於 PDF「基於 V2I 和 V2V 模式的車輛邊緣計算的聯合任務卸載和資源分配」中的信息，以下是一個演示結構，其中包含三張幻燈片，重點關注「模擬結果」和「性能評估」：
Slide 1: Overview of Simulation Setup投影片 1：模擬設定概述
Title: Simulation Parameters and Setup標題：仿真參數與設定
Content:內容：
Simulation Environment:模擬環境：
- The simulations model a bidirectional urban expressway with a single Road Side Unit (RSU) and multiple moving vehicles該模擬模擬了一條具有單一路邊單元 (RSU) 和多個移動車輛的雙向城市高速公路.。
- Vehicle velocities are modeled using a truncated Gaussian distribution to reflect realistic urban expressway conditions車輛速度使用截斷高斯分佈進行建模，以反映現實的城市高速公路狀況.。
Simulation Tools and Parameters:仿真工具和參數：
- MATLAB is utilized for numerical-based simulations, with parameters derived from practical experiments to ensure reliability and applicability利用MATLAB進行數值模擬，參數來自實際實驗，確保可靠性與適用性.。
Visuals:視覺效果：
- Diagram of the simulation environment layout.模擬環境佈局圖。
- Table of key simulation parameters used.使用的關鍵模擬參數表。
Slide 2: Key Simulation Results投影片 2：主要模擬結果
Title: Analysis of Simulation Outcomes標題：模擬結果分析
Content:內容：
Performance of Offloading Algorithms:卸載演算法的效能：
- The OJTR and HJTR algorithms significantly outperform other schemes such as Local Computing Only (LCO) and RSU Assistance Only (RAO), particularly in handling higher vehicle densities and larger data sizesOJTR 和 HJTR 演算法顯著優於其他方案，例如僅本地計算 (LCO) 和僅 RSU 輔助 (RAO)，特別是在處理更高的車輛密度和更大的數據量方面。.。
Impact of Vehicle Density and Task Data Size:車輛密度和任務資料大小的影響：
- Increased vehicle numbers and task data sizes lead to higher total delays, but the proposed algorithms manage these delays more effectively than comparative schemes車輛數量和任務資料大小的增加會導致總延遲增加，但所提出的演算法比比較方案更有效地管理這些延遲.。
Visuals:視覺效果：
- Graphs showing total delay trends with changes in vehicle numbers and data sizes.顯示總延誤趨勢隨車輛數量和資料大小變化的圖表。
- Comparative analysis highlighting the efficiency of OJTR and HJTR over other methods.比較分析強調了 OJTR 和 HJTR 相對於其他方法的效率。
Slide 3: Performance Evaluation and Insights投影片 3：績效評估與見解
Title: Evaluating System Performance標題：評估系統效能
Content:內容：
Algorithm Efficiency and Cost:演算法效率和成本：
- Time consumption and iteration counts for algorithms, illustrating OJTR’s higher efficiency despite greater complexity演算法的時間消耗和迭代計數，說明 OJTR 儘管複雜性更高，但效率更高.。
- Performance under different computational and channel resource settings shows the adaptability and robustness of the proposed algorithms不同計算和通道資源設定下的效能顯示了所提出演算法的適應性和穩健性.。
Practical Implications:實際影響：
- The simulation confirms the practical applicability of the algorithms, suggesting potential for real-world deployment with even more powerful hardware configurations模擬證實了演算法的實際適用性，表明具有更強大的硬體配置的實際部署潛力.。
Visuals:視覺效果：
- Charts depicting algorithm performance metrics.描述算法性能指標的圖表。
- Insights on potential improvements and hardware configurations for practical deployment.有關實際部署的潛在改進和硬體配置的見解。
Summary: This presentation provides a detailed and systematic exploration of the simulation results and performance evaluations from the study. It illustrates how the proposed algorithms optimize task offloading and resource allocation in a VEC environment, thereby enhancing overall system performance and efficiency. The visuals and summarized points ensure that the presentation is accessible, engaging, and informative for the seminar audience.概括： 本演講對研究的模擬結果和性能評估進行了詳細和系統的探索。它說明了所提出的演算法如何優化 VEC 環境中的任務卸載和資源分配，從而提高整體系統效能和效率。視覺效果和總結要點確保簡報對於研討會觀眾來說是易於理解、有吸引力且資訊豐富的。
For your seminar presentation based on the provided PDF file on "Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes," here are detailed descriptions for each slide that you can include in your memo. These descriptions will ensure that each slide effectively communicates the key findings and insights from the simulation results and performance evaluation as outlined in the PDF.對於基於所提供的 PDF 文件「基於 V2I 和 V2V 模式的車輛邊緣計算的聯合任務卸載和資源分配」的研討會演示，以下是您可以在備忘錄中包含的每張幻燈片的詳細說明。這些描述將確保每張投影片有效傳達 PDF 中概述的模擬結果和效能評估的主要發現和見解。
Slide 1: Overview of Simulation Setup投影片 1：模擬設定概述
Title: Simulation Parameters and Setup標題：仿真參數與設定
Description: This slide provides an overview of the simulation setup used to evaluate the proposed vehicular edge computing algorithms within a controlled environment. The simulations were conducted in a modeled urban expressway setting with vehicles and a centrally located Road Side Unit (RSU). Vehicle velocities were simulated using a truncated Gaussian distribution to mimic realistic urban mobility patterns. MATLAB was chosen for its robustness in handling complex numerical simulations, ensuring that the parameters and results are both reliable and applicable to real-world scenarios.描述： 本投影片概述了用於在受控環境中評估所提出的車輛邊緣運算演算法的模擬設定。模擬是在帶有車輛和位於中心的路邊單元 (RSU) 的城市高速公路模型環境中進行的。使用截斷高斯分佈來模擬車輛速度，以模仿現實的城市交通模式。選擇 MATLAB 是因為它在處理複雜數值模擬方面具有穩健性，可確保參數和結果既可靠又適用於現實情境。
Visuals:視覺效果：
- A diagram illustrates the layout of the simulated urban expressway environment, showing the RSU and vehicle movements.該圖展示了模擬城市快速道路環境的佈局，顯示了 RSU 和車輛運動。
- A table lists the key parameters used in the simulations, such as vehicle speed distribution, data sizes, and computational resources, providing clarity on the variables influencing the simulation outcomes.表格列出了模擬中使用的關鍵參數，例如車速分佈、資料大小和運算資源，清楚地說明了影響模擬結果的變數。
Slide 2: Key Simulation Results投影片 2：主要模擬結果
Title: Analysis of Simulation Outcomes標題：模擬結果分析
Description: This slide presents critical outcomes from the simulation experiments, focusing on the performance of the Optimal Joint Task Offloading and Resource Allocation (OJTR) and Heuristic Joint Task Offloading and Resource Allocation (HJTR) algorithms compared to other existing methods like Local Computing Only (LCO) and RSU Assistance Only (RAO). It highlights how the proposed algorithms efficiently handle increasing vehicle densities and task data sizes, effectively managing delays compared to the alternatives.描述： 本投影片介紹了模擬實驗的關鍵結果，重點在於最佳聯合任務卸載和資源分配(OJTR) 和啟發式聯合任務卸載和資源分配(HJTR) 演算法與其他現有方法（例如僅本地計算(LCO) ）相比的性能僅 RSU 援助 (RAO)。它強調了所提出的演算法如何有效地處理不斷增加的車輛密度和任務資料大小，與替代方案相比，有效管理延遲。
Visuals:視覺效果：
- Graphs display the trends in total processing delays as vehicle numbers and task sizes increase, illustrating the superior performance of the proposed algorithms.圖表顯示了隨著車輛數量和任務規模的增加總處理延遲的趨勢，說明了所提出演算法的優越性能。
- A comparative analysis chart provides a visual summary of the efficiency of OJTR and HJTR against other methods, emphasizing their advantages in reducing task processing times.比較分析圖直觀地總結了 OJTR 和 HJTR 相對於其他方法的效率，強調了它們在減少任務處理時間方面的優勢。
Slide 3: Performance Evaluation and Insights投影片 3：績效評估與見解
Title: Evaluating System Performance標題：評估系統效能
Description: The final slide evaluates the efficiency and operational cost of the algorithms, detailing the computational time and number of iterations required to achieve optimal results. It assesses the algorithms’ performance under varying computational and channel resource conditions, showcasing their adaptability and robustness. The slide concludes with practical implications derived from the simulation, suggesting the feasibility of real-world applications and potential enhancements with more advanced hardware configurations.描述： 最後一張投影片評估了演算法的效率和營運成本，詳細說明了實現最佳結果所需的計算時間和迭代次數。它評估了演算法在不同計算和通道資源條件下的性能，展示了它們的適應性和穩健性。這張投影片最後給出了模擬得出的實際意義，顯示了實際應用的可行性以及更先進的硬體配置的潛在增強功能。
Visuals:視覺效果：
- Performance metrics such as time consumption and iteration counts are depicted in charts, showing the detailed performance evaluation of the algorithms.時間消耗和迭代次數等效能指標以圖表形式描述，顯示了演算法的詳細效能評估。
- Insightful comments on potential practical deployments and hardware enhancements are provided, indicating directions for future research and application.提供了對潛在的實際部署和硬體增強的富有洞察力的評論，指出了未來研究和應用的方向。
Summary: Each slide in this presentation is meticulously crafted to convey the findings from the simulation studies detailed in the provided PDF. The presentation aims to provide a comprehensive, accessible, and engaging summary of how the proposed algorithms enhance task offloading and resource allocation in vehicular edge computing environments. The detailed descriptions and visuals ensure that seminar attendees can grasp the essential points without needing to read the full document.概括： 本簡報中的每張投影片都經過精心製作，旨在傳達所提供的 PDF 中詳細介紹的模擬研究的結果。該演示旨在提供全面、易於理解且引人入勝的摘要，介紹所提出的演算法如何增強車輛邊緣運算環境中的任務卸載和資源分配。詳細的描述和視覺效果確保研討會與會者無需閱讀完整文件即可掌握要點。
Based on the information from the PDF "Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes," here's how you can structure a two-slide presentation focusing on the "Conclusion" and "Recommended Research Directions":根據 PDF「基於 V2I 和 V2V 模式的車輛邊緣計算的聯合任務卸載和資源分配」中的信息，您可以按照以下方式構建側重於「結論」和「建議研究方向」的兩幻燈片簡報：
Slide 1: Conclusion投影片 1：結論
Title: Outcomes and Achievements標題：成果和成就
Content:內容：
Summary of Findings:調查結果摘要：
- The paper proposes a comprehensive joint resource management scheme that minimizes total task processing delays through optimal scheduling, wireless channel allocation, and computing resource distribution among vehicles and an RSU.本文提出了一種全面的聯合資源管理方案，透過優化調度、無線通道分配以及車輛和 RSU 之間的運算資源分配來最大限度地減少總任務處理延遲。
- It employs RL (Reinforcement Learning)-and-GBD (Generalized Benders Decomposition)-based algorithms to deliver optimal and sub-optimal solutions.它採用基於 RL（強化學習）和 GBD（廣義 Benders 分解）的演算法來提供最優和次優解。
Results of Simulations:模擬結果：
- Extensive simulations demonstrate significant performance improvements over four comparative schemes, confirming the effectiveness of the proposed approach in real-world vehicular settings.廣泛的模擬證明了四種比較方案的顯著性能改進，證實了所提出的方法在現實車輛環境中的有效性。
Visuals:視覺效果：
- Bar graph showing performance comparison between the proposed methods and other schemes.長條圖顯示了所提出的方法和其他方案之間的性能比較。
- Key statistics highlighting reduction in task processing delays.強調任務處理延遲減少的關鍵統計資料。
Slide 2: Recommended Research Directions投影片 2：推薦研究方向
Title: Future Work and Extensions標題：未來的工作與擴展
Content:內容：
Extension of Current Work:目前工作的擴展：
- Plans to extend the current framework to scenarios involving multiple RSUs with task results relaying capabilities, enhancing the system's robustness and efficiency .計劃將當前框架擴展到涉及多個具有任務結果中繼能力的RSU的場景，增強系統的穩健性和效率。
- Exploration of time-varying or stochastic V2V channel gains due to vehicle mobility, aiming to refine the accuracy of channel modeling and resource allocation .探索車輛移動性導致的時變或隨機 V2V 通道增益，旨在提高通道建模和資源分配的準確性。
Further Research Areas:進一步的研究領域：
- Development of adaptive algorithms that can dynamically respond to changes in vehicular density and network conditions.開發能夠動態響應車輛密度和網路條件變化的自適應演算法。
- Investigation into the integration of AI techniques for predictive analytics in resource allocation.研究人工智慧技術在資源分配中的預測分析整合。
Visuals:視覺效果：
- Conceptual diagrams illustrating the extended network scenarios with multiple RSUs.概念圖展示了具有多個 RSU 的擴展網路場景。
- Timeline for future research activities and expected milestones.未來研究活動和預期里程碑的時間表。
Summary: This presentation encapsulates the core conclusions drawn from the study and outlines clear pathways for future research. It emphasizes the study’s practical implications and potential extensions, making it straightforward for the seminar audience to grasp the significance of the findings and the directions for further investigations.概括： 本演講概括了該研究得出的核心結論，並概述了未來研究的明確途徑。它強調了研究的實際意義和潛在的擴展，使研討會聽眾能夠輕鬆掌握研究結果的意義和進一步研究的方向。
For your seminar presentation based on the provided PDF file on "Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes," here are detailed descriptions for each slide that you can include in your memo. These descriptions are tailored to ensure that each slide clearly and authentically communicates the conclusion of the study and suggests future research directions.對於基於所提供的 PDF 文件「基於 V2I 和 V2V 模式的車輛邊緣計算的聯合任務卸載和資源分配」的研討會演示，以下是您可以在備忘錄中包含的每張幻燈片的詳細說明。這些描述經過專門設計，以確保每張投影片都清晰、真實地傳達研究結論並提出未來的研究方向。
Slide 1: Conclusion投影片 1：結論
Title: Outcomes and Achievements標題：成果和成就
Description: This slide synthesizes the major findings and achievements detailed in the study, emphasizing the development and implications of a comprehensive joint resource management scheme. It underlines the utilization of RL (Reinforcement Learning)-and-GBD (Generalized Benders Decomposition)-based algorithms that have been specifically tailored to optimize task scheduling, wireless channel allocation, and computing resource distribution among vehicles and RSUs. The conclusion also discusses the results from extensive simulations that validate the effectiveness of the proposed methodologies compared to four other existing schemes, demonstrating notable improvements in minimizing total task processing delays within real-world vehicular environments.描述： 這張投影片綜合了研究中詳細介紹的主要發現和成就，強調了綜合聯合資源管理計畫的發展和影響。它強調使用基於 RL（強化學習）和 GBD（廣義 Benders 分解）的演算法，這些演算法專門用於優化任務調度、無線通道分配以及車輛和 RSU 之間的計算資源分配。結論還討論了廣泛模擬的結果，這些模擬驗證了所提出的方法與其他四種現有方案相比的有效性，證明了在最大限度地減少現實車輛環境中總任務處理延遲方面的顯著改進。
Visuals:視覺效果：
- A bar graph visually contrasting the performance of the proposed methods against other schemes, highlighting superior outcomes in reducing task processing times.長條圖直觀地將所提出的方法與其他方案的效能進行對比，突顯在減少任務處理時間方面的卓越成果。
- Key statistics and quantitative results that underscore the significant reductions in processing delays achieved through the implementation of the proposed solutions.關鍵統計數據和定量結果強調透過實施所提出的解決方案可顯著減少處理延遲。
Slide 2: Recommended Research Directions投影片 2：推薦研究方向
Title: Future Work and Extensions標題：未來的工作與擴展
Description: The second slide outlines prospective research directions that build upon the current study's findings. It presents plans for expanding the existing framework to include multiple RSUs with enhanced task relaying capabilities, aiming to increase the robustness and efficiency of the vehicular network system. Additionally, it proposes investigating more dynamic channel models to accommodate time-varying or stochastic V2V channel gains, which are influenced by vehicle mobility. This slide also suggests further exploration into adaptive algorithms that can dynamically adjust to changing vehicular densities and network conditions, as well as the potential application of AI techniques to predictively manage resources more efficiently.描述： 第二張投影片概述了基於目前研究結果的前瞻性研究方向。它提出了擴展現有框架的計劃，以包括多個具有增強任務中繼能力的 RSU，旨在提高車輛網路系統的穩健性和效率。此外，它還建議研究更動態的通道模型，以適應受車輛移動性影響的時變或隨機 V2V 通道增益。這張投影片還建議進一步探索自適應演算法，這些演算法可以動態調整以適應不斷變化的車輛密度和網路條件，以及人工智慧技術的潛在應用，以更有效地預測性管理資源。
Visuals:視覺效果：
- Conceptual diagrams that depict extended network scenarios involving multiple RSUs, providing a visual representation of the proposed enhancements.概念圖描繪了涉及多個 RSU 的擴展網路場景，提供了所提議的增強功能的可視化表示。
- A timeline that outlines future research activities and the expected milestones, helping to convey a structured plan for ongoing investigations.概述未來研究活動和預期里程碑的時間表，有助於傳達正在進行的調查的結構化計劃。
Summary: These slides are designed to provide a comprehensive summary of the study's conclusions and to articulate clear directions for future research. The detailed descriptions and supporting visuals are crafted to ensure that the presentation is not only informative and credible but also engaging and accessible to the seminar audience, facilitating a deep understanding of the study's impact and future potential without needing to refer to the full document.概括： 這些投影片旨在提供研究結論的全面總結，並闡明未來研究的明確方向。詳細的描述和支援視覺效果經過精心設計，以確保簡報不僅資訊豐富、可信，而且對研討會觀眾有吸引力且易於理解，有助於深入了解研究的影響和未來潛力，而無需參考完整的文件。