scholarly journals DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework

2021 ◽  
Vol 14 (1) ◽  
pp. 13
Author(s):  
Volkov Artem ◽  
Kovalenko Vadim ◽  
Ibrahim A. Elgendy ◽  
Ammar Muthanna ◽  
Andrey Koucheryavy
Keyword(s):  
Communication Networks ◽  
Autonomous Vehicles ◽  
Fog Computing ◽  
Complex Nature ◽  
Application Development ◽  
High Data ◽  
Search Mechanism ◽  
User Query ◽  
Rational Allocation ◽  
Delivery Services

Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.

scholarly journals Millimeter Wave Spatial Channel Characterization for Vehicular Communications

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 64 ◽  
Author(s):  
Fidel Rodríguez-Corbo ◽  
Leyre Azpilicueta ◽  
Mikel Celaya-Echarri ◽  
Peio López-Iturri ◽  
Imanol Picallo ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Autonomous Vehicles ◽  
Communication Systems ◽  
Vehicular Networks ◽  
Small Scale ◽  
Vehicular Communications ◽  
Next Generation ◽  
Vehicular Communication ◽  
High Definition ◽  
High Data

With the growing demand of vehicle-mounted sensors over the last years, the amount of critical data communications has increased significantly. Developing applications such as autonomous vehicles, drones or real-time high-definition entertainment requires high data-rates in the order of multiple Gbps. In the next generation of vehicle-to-everything (V2X) networks, a wider bandwidth will be needed, as well as more precise localization capabilities and lower transmission latencies than current vehicular communication systems due to safety application requirements; 5G millimeter wave (mmWave) technology is envisioned to be the key factor in the development of this next generation of vehicular communications. However, the implementation of mmWave links arises with difficulties due to blocking effects between mmWave transceivers, as well as different channel impairments for these high frequency bands. In this work, the mmWave channel propagation characterization for V2X communications has been performed by means of a deterministic in-house 3D ray launching simulation technique. A complex heterogeneous urban scenario has been modeled to analyze the different propagation phenomena of multiple mmWave V2X links. Results for large and small-scale propagation effects are obtained for line-of-sight (LOS) and non-LOS (NLOS) trajectories, enabling inter-data vehicular comparison. These analyzed results and the proposed methodology can aid in an adequate design and implementation of next generation vehicular networks.

scholarly journals Tiarrah Computing: The Next Generation of Computing

2018 ◽  
Vol 8 (2) ◽  
pp. 1247
Author(s):  
Yanish Pradhananga ◽  
Pothuraju Rajarajeswari
Keyword(s):  
Real Time ◽  
High Performance ◽  
New Technologies ◽  
Fog Computing ◽  
High Availability ◽  
Application Development ◽  
Dynamic Cluster ◽  
Event Handling ◽  
Application Deployment ◽  
Flexible Application

The evolution of Internet of Things (IoT) brought about several challenges for the existing Hardware, Network and Application development. Some of these are handling real-time streaming and batch bigdata, real- time event handling, dynamic cluster resource allocation for computation, Wired and Wireless Network of Things etc. In order to combat these technicalities, many new technologies and strategies are being developed. Tiarrah Computing comes up with integration the concept of Cloud Computing, Fog Computing and Edge Computing. The main objectives of Tiarrah Computing are to decouple application deployment and achieve High Performance, Flexible Application Development, High Availability, Ease of Development, Ease of Maintenances etc. Tiarrah Computing focus on using the existing opensource technologies to overcome the challenges that evolve along with IoT. This paper gives you overview of the technologies and design your application as well as elaborate how to overcome most of existing challenge.

scholarly journals An Efficient Multi-Vehicle Routing Strategy for Goods Delivery Services

2021 ◽  
Author(s):  
Linh Nguyen
Keyword(s):  
Autonomous Vehicles ◽  
Optimization Problem ◽  
Transportation Cost ◽  
Mixed Integer ◽  
Optimal Routing ◽  
Ground Vehicles ◽  
Routing Optimization ◽  
Routing Strategy ◽  
Delivery Services ◽  
Delivery Vehicles

<pre>The paper addresses the problem of efficiently planning routes for multiple ground vehicles used in goods delivery services. Given popularity of today's e-commerce, particularly under the COVID-19 pandemic conditions, goods delivery services have been booming than ever, dominated by small-scaled (electric) bikes and promised by autonomous vehicles. However, finding optimal routing paths for multiple delivery vehicles operating simultaneously in order to minimize transportation cost is a fundamental but challenging problem. In this paper, it is first proposed to exploit the mixed integer programming paradigm to model the delivery routing optimization problem (DROP) for multiple simultaneously-operating vehicles given their energy constraints. The routing optimization problem is then solved by the multi-chromosome genetic algorithm, where the number of delivery vehicles can be optimized. The proposed approach was evaluated in a real-world experiment in which goods were expected to be delivered from a depot to 26 suburb locations in Canberra, Australia. The obtained results demonstrate effectiveness of the proposed algorithm.</pre>

scholarly journals Latency-Aware Offloading in Integrated Satellite Terrestrial Networks

2020 ◽  
Author(s):  
Wiem Abderrahim ◽  
Osama Amin ◽  
Mohamed-Slim Alouini ◽  
Basem Shihada
Keyword(s):  
Communication Networks ◽  
Rural Areas ◽  
Access Network ◽  
High Capacity ◽  
High Load ◽  
Traffic Load ◽  
Satellite Systems ◽  
High Data ◽  
Data Rates ◽  
Terrestrial Networks

Next-generation communication networks are expected to integrate newly-used technologies in a smart way to ensure continuous connectivity in rural areas and to alleviate the traffic load in dense regions. The prospective access network in 6G should hinge on satellite systems to take advantage of their wide coverage and high capacity. However, adopting satellites in 6G could be hindered because of the {additional latency introduced}, which is not tolerable by all traffic types. Therefore, we propose a traffic offloading scheme that integrates both the satellite and terrestrial networks to smartly allocate the traffic between them while satisfying different traffic requirements. Specifically, the proposed scheme offloads the Ultra-Reliable Low Latency Communication (URLLC) traffic to the terrestrial backhaul to satisfy its stringent latency requirement. However, it offloads the enhanced Mobile Broadband (eMBB) traffic to the satellite since eMBB needs high data rates but is not always sensitive to delay. Our scheme is shown to reduce the transmission delay of URLLC packets, decrease the number of dropped eMBB packets, and hence improve the network's availability. Our findings highlight that the inter-working between satellite and terrestrial networks is crucial to mitigate the expected high load on the limited terrestrial capacity.<br>

scholarly journals Time-Sensitive Network (TSN) Experiment in Sensor-Based Integrated Environment for Autonomous Driving

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1111 ◽  
Author(s):  
Juho Lee ◽  
Sungkwon Park
Keyword(s):  
Communication Networks ◽  
Autonomous Vehicles ◽  
Performance Test ◽  
Autonomous Driving ◽  
Navigation Systems ◽  
Data Traffic ◽  
Control Units ◽  
Band Size ◽  
Integrated Environment ◽  
Vehicle Network

Recently, large amounts of data traffic from various sensors and image and navigation systems within vehicles are generated for autonomous driving. Broadband communication networks within vehicles have become necessary. New autonomous Ethernet networks are being considered as alternatives. The Ethernet-based in-vehicle network has been standardized in the IEEE 802.1 time-sensitive network (TSN) group since 2006. The Ethernet TSN will be revised and integrated into a subsequent version of IEEE 802.1Q-2018 published in 2018 when various new TSN-related standards are being newly revised and published. A TSN integrated environment simulator is developed in this paper to implement the main functions of the TSN standards that are being developed. This effort would minimize the performance gaps that can occur when the functions of these standards operate in an integrated environment. As part of this purpose, we analyzed the simulator to verify that the traffic for autonomous driving satisfies the TSN transmission requirements in the in-vehicle network (IVN) and the preemption (which is one of the main TSN functions) and reduces the overall End-to-End delay. An optimal guard band size for the preemption was also found for autonomous vehicles in our work. Finally, an IVN model for autonomous vehicles was designed and the performance test was conducted by configuring the traffic to be used for various sensors and electronic control units (ECUs).

scholarly journals Development Innovation to Predict Dengue Affected Area and Alert People with Smartphones

2020 ◽  
Vol 16 (02) ◽  
pp. 62 ◽  
Author(s):  
Patsaraporn Somboonsak
Keyword(s):  
Dengue Fever ◽  
Fog Computing ◽  
Arima Model ◽  
Application Development ◽  
Vector Borne Diseases ◽  
Traffic Lights ◽  
The People ◽  
Dengue Outbreaks ◽  
Vector Borne ◽  
Self Driving Cars

<p class="0abstract">Dengue remains a significant problem that needs to be addressed urgently in Thailand. Although Thailand has spread the dengue fever for more than sixty years, however, it is still found dengue patients in every province and spread to various areas. There is also a variable pattern of disease occurring each year, so it is necessary to have tools to help forecast area to allow the related organization and the people in the area plan to prevent dengue fever that may occur next year. This research aimed to create innovation for predicting dengue fever regions, namely ThaiDengue, by collecting data from dengue patients in Chatuchak District, Bangkok, Thailand, from January 2014 to December 2018. There was a total of 358,524 dengue patients from the Bureau of vector-borne diseases applied to the prediction of patients in the next year with the ARIMA model (1,1,0) (1,1,0). It is predicted that in 2019, Thailand will have dengue patients around 95,000 cases, which has the number of dengue patients close to the year 2018. In the next step, application development and database on fog computing. Fog computing is an evolving technology that brings the benefits achieved by could computing to the periphery of the network devices for faster data analytics. It is better suited than cloud computing for meeting the demands of numerous emerging applications such as self-driving cars, traffic lights, smart homes. While the ThaiDengue consists of the main menu: how to use, forecast, surveillance calendar, notification, disease map, notify patients, contact the Bureau of vector-borne, knowledge information, and scan the QR code. After that, the result of the development, the researcher has the Bureau of vector-borne disease of Thailand used to forecasts, create a GPS map of dengue outbreaks, and create a calendar for dengue monitoring.  After that, send a message to alert the people in the area of dengue via a smartphone and send additional emails. The results from using the application found this application can be used as a tool to help the Bureau of vector-borne diseases, to plan dengue fever control and alert the people in the risk areas of dengue outbreak and users are very satisfied with the use of the application.</p>

scholarly journals Multilevel Service-Provisioning-Based Autonomous Vehicle Applications

2020 ◽  
Vol 12 (6) ◽  
pp. 2497 ◽  
Author(s):  
Mashael Khayyat ◽  
Abdullah Alshahrani ◽  
Soltan Alharbi ◽  
Ibrahim Elgendy ◽  
Alexander Paramonov ◽  
...  
Keyword(s):  
Communication Networks ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Access Network ◽  
High Mobility ◽  
Base Stations ◽  
Traffic Density ◽  
Fifth Generation ◽  
Radio Access ◽  
Vehicle Networks

With the recent advances and development of autonomous control systems of cars, the design and development of reliable infrastructure and communication networks become a necessity. The recent release of the fifth-generation cellular system (5G) promises to provide a step towards reliability or a panacea. However, designing autonomous vehicle networks has more requirements due to the high mobility and traffic density of such networks and the latency and reliability requirements of applications run over such networks. To this end, we proposed a multilevel cloud system for autonomous vehicles which was built over the Tactile Internet. In addition, base stations at the edge of the radio-access network (RAN) with different technologies of antennas are used in our system. Finally, simulation results show that the proposed system with multilevel clouding can significantly reduce the round-trip latency and the network congestion. In addition, our system can be adapted in the mobility scenario.

scholarly journals Design and Implementation of Edge-Fog-Cloud System through HD Map Generation from LiDAR Data of Autonomous Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2084
Author(s):  
Junwon Lee ◽  
Kieun Lee ◽  
Aelee Yoo ◽  
Changjoo Moon
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Fog Computing ◽  
Point Clouds ◽  
Lidar Data ◽  
High Definition ◽  
Localization And Mapping ◽  
Cloud Server ◽  
Map Generation ◽  
Self Driving Cars

Self-driving cars, autonomous vehicles (AVs), and connected cars combine the Internet of Things (IoT) and automobile technologies, thus contributing to the development of society. However, processing the big data generated by AVs is a challenge due to overloading issues. Additionally, near real-time/real-time IoT services play a significant role in vehicle safety. Therefore, the architecture of an IoT system that collects and processes data, and provides services for vehicle driving, is an important consideration. In this study, we propose a fog computing server model that generates a high-definition (HD) map using light detection and ranging (LiDAR) data generated from an AV. The driving vehicle edge node transmits the LiDAR point cloud information to the fog server through a wireless network. The fog server generates an HD map by applying the Normal Distribution Transform-Simultaneous Localization and Mapping(NDT-SLAM) algorithm to the point clouds transmitted from the multiple edge nodes. Subsequently, the coordinate information of the HD map generated in the sensor frame is converted to the coordinate information of the global frame and transmitted to the cloud server. Then, the cloud server creates an HD map by integrating the collected point clouds using coordinate information.

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