Edge-MapReduce-Based Intelligent Information-Centric IoV: Cognitive Route Planning

Zhao, Chengcheng; Dong, Mianxiong; Ota, Kaoru; Li, Jianhua; Wu, Jun

doi:10.1109/access.2019.2911343

Cited by 27 publications

(11 citation statements)

References 37 publications

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“…The work of Xiaoxue et al [24] proposed a path planning strategy based on a tradeoff between the path length and time balance to carry out optimal path planning. In [25], the authors proposed an edge-based big data analysis architecture to gather vehicle information and traffic status to provide the optimal driving path to vehicles. Zhang et al [26] designed a route-planning algorithm based on real-time data, such as fuel consumption, road patterns, and feedback of drivers.…”

Section: Featuresmentioning

confidence: 99%

SEARCH: An SDN-Enabled Approach for Vehicle Path-Planning

Oubbati

Atiquzzaman

Lorenz

et al. 2020

IEEE Trans. Veh. Technol.

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With increasing vehicle density and the growth of accidents in urban areas, navigation management becomes a serious problem. Even though there is a multitude of navigation systems, ambulances, taxis, or even ordinary vehicles, sometimes find it challenging to reach their destinations on time. There are two main reasons for this difficulty: (i) lack of local knowledge of the area of navigation solutions and (ii) their inflexibility against unforeseeable situations that may occur on the roads. Indeed, the majority of navigation solutions are based only on the distance, the journey time, or even statistics related to the density of vehicles to plan the full paths, while neglecting the dynamic nature of the vehicle traffic. Also, their respective centralized architectures are unable to monitor both the traffic and unexpected events continuously and in real-time without not being overloaded by the flow of message exchanges between the road entities and the central processing entity. To address these issues, we propose in this paper a novel three-tier architecture, called SDN-enabled Approach for Vehicle Path-Planning (SEARCH), to enhance the situation awareness on urban roads, efficiently collect traffic information in real-time, and decide the best navigation strategy. The proposed architecture exploits Unmanned Aerial Vehicles (UAVs), Vehicular Ad hoc Networks (VANETs), 5G based cellular systems, and Software-Defined Networking (SDN) to provide better and faster communication to changing road conditions. Based on these technologies, some parameters related to vehicles and driving environments, such as speed, distance, traffic jams, incidents, and travel flow, are efficiently collected and dynamically exploited to achieve faster paths between any existing pairs of locations. Furthermore, the deployed architecture of SEARCH can provide sufficient bandwidth to support all data traffic needs to update vehicles during their journey efficiently and in real-time. To evaluate the performance of our architecture, we conduct a series of simulations and perform a set of comparisons with relevant route planning algorithms. We found that the proposed architecture works effectively in terms of saving on driving time to reach any target destinations.

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Section: Featuresmentioning

confidence: 99%

SEARCH: An SDN-Enabled Approach for Vehicle Path-Planning

Oubbati

Atiquzzaman

Lorenz

et al. 2020

IEEE Trans. Veh. Technol.

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“…It relies on location independent naming, in-network caching, name-based routing and data selfsecurity for effective content distribution across the whole network. This fact allows mobility support by nature [59]. Therefore, in order to overcome the above problems, ICN in edge computing is promising.…”

Section: B Icn In Edge Computingmentioning

confidence: 99%

Information-Centric Networking: Research and Standardization Status

Eum

Kurita

et al. 2019

IEEE Access

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Information-centric networking (ICN) is a new approach to networking contents rather than devices that hold the contents. It has recently attracted much attention of network research and standardization communities. National and multi-national funded research projects have progressed worldwide. International Telecommunication Union -Telecommunication Standardization Sector (ITU-T) started ICN standardization activities in 2012. In parallel, the standards-oriented research cooperation is progressing in the Information-Centric Networking Research Group (ICNRG) of the Internet Research Task Force (IRTF). All these global efforts have been collectively advancing the novel network architecture of ICN. However, there are very few surveys and discussions on the detailed ICN standardization status. To update the reader with information about the ICN research and standardization related activities, this paper starts with the history of global activities on ICN from 2010, giving references to various projects. It then describes the recent progress in the standardization of ICN component technologies in ITU-T and various documents produced by ICNRG. Lastly, it discusses the future directions for progressing ICN.

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“…The cost model includes the communication cost which is the amount of data transmitted during MapReduce computations and the replication rate which represents the number of key-value pairs generated by all the mapper functions, divided by the number of inputs. A growing number of papers deal with MapReduce algorithms for various problems [17][18][19][20]. Recently, Grahne et al [21,22] have implemented efficiently the intersection and the minimization operations of finite automata in MapReduce.…”

Section: Introductionmentioning

confidence: 99%

Composition of weighted finite transducers in MapReduce

2021

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Weighted finite-state transducers have been shown to be a general and efficient representation in many applications such as text and speech processing, computational biology, and machine learning. The composition of weighted finite-state transducers constitutes a fundamental and common operation between these applications. The NP-hardness of the composition computation problem presents a challenge that leads us to devise efficient algorithms on a large scale when considering more than two transducers. This paper describes a parallel computation of weighted finite transducers composition in MapReduce framework. To the best of our knowledge, this paper is the first to tackle this task using MapReduce methods. First, we analyze the communication cost of this problem using Afrati et al. model. Then, we propose three MapReduce methods based respectively on input alphabet mapping, state mapping, and hybrid mapping. Finally, intensive experiments on a wide range of weighted finite-state transducers are conducted to compare the proposed methods and show their efficiency for large-scale data.

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Edge-MapReduce-Based Intelligent Information-Centric IoV: Cognitive Route Planning

Cited by 27 publications

References 37 publications

SEARCH: An SDN-Enabled Approach for Vehicle Path-Planning

SEARCH: An SDN-Enabled Approach for Vehicle Path-Planning

Information-Centric Networking: Research and Standardization Status

Composition of weighted finite transducers in MapReduce

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