Routing Strategy for Internet of Vehicles based on Hierarchical SDN and Fog Computing

تم اکتشاف الحوسبة الضبابیة لحل مشكلة نقص المصادر في مستشعرات إنترنت الأشياء (IoT) ومعالجة المهام بسرعة. انترنت المركبات (IoV) هو تطبيق خاص من شبكات إنترنت الأشياء التي تتكون من أجهزة استشعار غير متجانسة موجودة في المركبات. تقوم هذه المستشعرات بنقل المهام إلى خوادم الحوسبة الضابية التي تعالجها وتعطي الإجابات للمستشعرات.  على اي حال، فإن حركة المركبات تؤثر على عملية تسليم هذه الاجابات. عندما تخرج السيارة المصدرة للمهمة من مجال خادم ضبابي معين خلال وقت معالجة هذه المهمة، فأنه لن يتم وصول الاجابة لتلك السيارة بشكل صحيح. لذلك، يحتاج إلى حساب المسار الأمثل لتلك السيارة. تتسبب هذه العملية في تجاوز الموعد النهائي للمهمة وتقليل الإنتاجية. للتغلب على هذه المشكلة، يقدم هذا البحث معمارية هرمية مبنية على الشبكات المعرفة بالبرمجيات (SDN) وحوسبة الضباب لشبكة IoV. تتألف هذه المعمارية من طبقة سيارات IoV, بيئة حوسبة ضبابية ووحدات تحكم SDN شبه مركزية ووحدة تحكم SDN  مركزية. علاوة على ذلك ، تم اقتراح إستراتيجية توجيهية تسمى إستراتيجية توجيه ذات تأخير جيد بالاعتماد على الحوسبة الضبابية و ال SDN لشبكات ال IoV (DRSFI) .تقوم وحدات التحكم SDN بتنفيذ DRSFI لحساب المسارات مع أدنى تأخير مع الأخذ بنظر الاعتبار قيد النطاق الترددي المتاح وموقع وسرعة المركبة. من نتائج محاكاة سيناريوهات مختلفة مع سرعات حركة متنوعة وعداد مختلفة من المهام، استنتجنا أن النظام المقترح أفضل من نظام IoV-Fog-Central SDN  ونظام IoV-Fog من حيث متوسط التأخير من البداية إلى النهاية و النسبة المئوية لخسارة الحزم والنسبة المئوية للإرسال الناجح.The fog computing is invited to solve the lack of resources problem in the sensors of Internet of Things (IoT) and handle the tasks quickly. Internet of Vehicles (IoV) is a special application of IoT networks that composed of heterogeneous sensors that are found in vehicles. These sensors transfer the tasks to the fog servers that process them and give the responses to the sensors. However, the mobility of vehicles effects on the delivery operation of responses. When the source vehicle of a task exited from the domain of some fog server through the processing time of this task, the response will not be reached to that vehicle correctly. Therefore, it is need to compute the optimal path to that vehicle. This process causes exceeding the task deadline and decreasing the throughput. To overcome this issue, this paper produces a hierarchical architecture based on Software Defined Network (SDN) and fog computing for IoV networks. This architecture consists of IoV vehicles, fog computing framework, semi-central SDN controllers and central SDN controller layers. Moreover, a routing strategy is proposed called Delay-Efficient Routing strategy based on SDN and Fog computing for IoV (DRSFI). The SDN controllers perform DRSFI to compute the routes with minimum delay with taking into consideration the available bandwidth constraint and the location and speed of the vehicle. From the results of simulation of different scenarios with various mobility speeds and various number of tasks, we concluded that the proposed system is better than IoV-Fog-central SDN system and IoV-Fog system in terms of average delay from end to end, percentage of packet loss and percentage of successfully transmission. &nbsp

Game Theory-based Allocation Management in VCC Networks

Vehicular Ad-hoc Networks (VANETs) have contributed significantly towards improving road traffic management and safety. VANETs, integrated with Vehicular Clouds, enable underutilized vehicular resources for efficient resource management, fulfilling service requests. However, due to the frequently changing network topology of vehicular cloud networks, the vehicles frequently move out of the coverage area of roadside units (RSUs), disconnecting from the RSUs and interrupting the fulfillment of ongoing service requests. In addition, working with heterogeneous vehicles makes it difficult to match the service requests with the varying resources of individual vehicles. Therefore, to address these challenges, this work introduces the concept of clustering resources from nearby vehicles to form Combined Resource Units (CRUs). These units contribute to maximizing the rate of fulfillment of service requests. CRU composition is helpful, especially for the heterogeneity of vehicles, since it allows clustering the varying resources of vehicles into a single unit. The vehicle resources are clustered into CRUs based on three different sized pools, making the service matching process more time-efficient. Previous works have adopted stochastic models for resource clustering configurations. However, this work adopts distinct search algorithms for CRU composition, which are computationally less complex. Results showed that light-weight search algorithms, such as selective search algorithm (SSA), achieved close to 80% of resource availability without over-assembling CRUs in higher density scenarios. Following CRU composition, a game-theoretical approach is opted for allocating CRUs to service requests. Under this approach, the CRUs play a non-cooperative game to maximize their utility, contributing to factors such as fairness, efficiency, improved system performance and reduced system overhead. The utility value takes into account the RSS (Received Signal Strength) value of each CRU and the resources required in fulfilling a request. Results of the game model showed that the proposed approach of CRU composition obtained 90% success rate towards matching and fulfilling service requests