Wireless backhauling in Intelligent Transportation Systems

Cicconetti, Claudio; Galeassi, Francesco; Mambrini, Raffaella; Rosellini, Michele; Rossi, Alessandro

doi:10.1109/itst.2011.6060067

Cited by 3 publications

(2 citation statements)

References 14 publications

(8 reference statements)

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“…Along with recent advances in wireless communication, vehicular networks have become an important 5G application . In fact, the explosive increasing vehicle data traffic has led to significant increases in vehicle‐to‐infrastructure (V2I) communication latency and imposed a heavy burden on backhaul links that connect local infrastructures to the core network (CN) . To alleviate the burden, dedicated short‐range communications (DSRC) have been developed as a standard for vehicle‐to‐everything (V2X) communication, with a 75‐MHz licensed spectrum at the 5.9‐GHz band in the United States .…”

Section: Introductionmentioning

confidence: 99%

Caching mechanism for mobile edge computing in V2I networks

Luo

Liwang

Huang

et al. 2019

Trans Emerging Tel Tech

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The caching-enabled mobile edge computing (MEC) system becomes increasingly crucial to the future long-term evolution vehicle-to-infrastructure (LTE-V2I) network, especially for fast delivery of popular content of delay-sensitive applications in the backhaul capacity-limited vehicular networks. Most related studies mainly focus on computation offloading strategy design and coding caching, while the deployment strategy design of caching capacities for high-speed vehicles has been overlooked. In this paper, we investigate the deployment problem in LTE-V2I networks with particular consideration on high-speed vehicles. First, in a one-way scenario, a joint optimization framework is formulated to minimize the cache size of the MEC system, meanwhile maximizing the average downloading percentage, where the MEC system's backhaul capacities, vehicle speeds, content popularity distributions, and sides of the highway are taken into account. The impacts of LTE-V2I system parameters, eg, vehicle speed, on the objective function are revealed through the one-way optimization. Then, the two-way optimization is formulated and solved by developing an iterative optimization approach based on the aforementioned one-way scenario. Particularly, the optimal caching allocation between two ways is derived. Simulation results validate the effectiveness of the proposed approaches even with low backhaul capacities and high vehicle speed.

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Section: Introductionmentioning

confidence: 99%

Caching mechanism for mobile edge computing in V2I networks

Luo

Liwang

Huang

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…transmit power optimization [4], the use of multiple antenna techniques [5], the proper allocation of time and frequency resources [6], and 2) realizing effective collaboration schemes among the OBUs for fast information disseminations, such as the use of network coding [7], and bit-torrent protocols borrowed from P2P networks [8]. Due to the high-speed vehicles, to meet the quality of service, it typically requires the backhaul capacity from the RSUs to the core networks should be large enough [9]. This is, however, difficult to realize although the expensive fiber cables or massive antennas techniques can be applicable.…”

Section: Introductionmentioning

confidence: 99%

Roadside-unit caching in vehicular ad hoc networks for efficient popular content delivery

Ding

Wang

Song

et al. 2015

2015 IEEE Wireless Communications and Networking Conference (WCNC)

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Driven by both personal and commercial interests, fast popular content delivery, as one of the key services offered by vehicular ad-hoc networks (VANETs), has recently received considerable attention. Most existing work mainly focuses on the resource allocation such as transmit power or subcarrier assignment from the on-board units (OBUs) to the roadside units (RSUs). However, due to the limited backhaul capacity, great efforts still need to be taken for delivering large-size files such as videos and music to the high speed vehicles. Motivated by the recent work of pre-storing files in the cell-edge base stations, in this paper, we address the efficient content delivery problems in VANET by caching popular files in the RSUs with large storage capacity. The main objective is to minimize the average time that an OBU downloads a file. We propose three algorithms of allocating files to RSUs, in the optimal, sub-optimal, and greedy ways respectively, where the first one can achieve the best performance, and the greedy one has the lowest complexity. We also analyze the average downloading time performance in terms of the number of RSUs, storage capacity, and vehicle speed. Simulation results indicate that the proposed RSU caching methods can significantly reduce the file-downloading time, and thus, improve the content delivery efficiency.

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Efficient data transfer in edge envisioned environment using artificial intelligence based edge node algorithm

Kumar

Batth

et al. 2020

Trans Emerging Tel Tech

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Edge computing technology has drawn the keystone of future intelligent transportation systems, especially in smart cities, because of processing data that are near to the user location present at the edge of the cloud server. Generally, in smart cities where distributed things have access to computational resources, data transfer becomes inevitable because of high latency, thereby resulting in crucial situations. Even though numerous technologies have emerged for improving the data communication among geo-distributed devices received from the cloud server but still, it lags in low learning performance. To address these challenges, an innovative artificial intelligence (AI) based edge node (E-Node) algorithm is implemented to optimize the edge to edge learning for well-organized data migration. To attain high reliability, AI-K-means neural network (KNN) and convolution neural network (CNN) is used initially for pre-processing and filtering the edge node. Further, the proposed E-Node algorithm outperforms the optimization technique effectively through the edge to edge computing The reliability performance is increased by reducing the node optimization time from 132 to 98 ms for 25 kb data and data transmission time is reduced from 93 to 45 ms for 80 kb data, thus reducing the latency in an edge envisioned environment. INTRODUCTIONArtificial intelligent (AI) frameworks help to solve problems involving the learning abilities of computers, language processing, and identifying various speech in the environment in a highly reliable and accurate manner. 1 Smart cities concept endures the cities with intelligence and other concepts involving smart vehicles, smart grid, and smart healthcare using the concept of deep learning. Internet of Vehicles (IoV) 2,3 lays the foundation for several new transportation systems . 4 IoV helps to improve the efficiency of transportation, reducing the accident rate and reducing the energy consumption in vehicles being reduced. 5,6 The data generated from these smart vehicles are stored in the cloud. However, it is difficult to handle several request messages from the client and process it, during a particular grave learning task. The proposed system uses a new model to foil centralized cloud computing, which reduces the latency during communication and improves the quality through the provision of various resources 7 that are near to terminal devices for latency-sensitive tasks. The use of edge to edge cooperative AI increases the learning efficiency, quality of service. It also improves variousAbbreviations: CNN, convolution neural network; E-Node, edge node; KNN, K-means neural network.

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Wireless backhauling in Intelligent Transportation Systems

Cited by 3 publications

References 14 publications

Caching mechanism for mobile edge computing in V2I networks

Caching mechanism for mobile edge computing in V2I networks

Roadside-unit caching in vehicular ad hoc networks for efficient popular content delivery

Efficient data transfer in edge envisioned environment using artificial intelligence based edge node algorithm

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