The Dynamics of Vehicular Networks in Large-Scale Urban Environments

2015 ◽  
Author(s):  
Nicholas Loulloudes ◽  
George Pallis ◽  
Marios D. Dikaiakos
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Urban Environments

Modeling the Impact of Mobility on the Connectivity of Vehicular Networks in Large-Scale Urban Environments

2016 ◽  
Vol 65 (4) ◽  
pp. 2753-2758 ◽  
Author(s):  
Xueshi Hou ◽  
Yong Li ◽  
Depeng Jin ◽  
Dapeng Oliver Wu ◽  
Sheng Chen
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Urban Environments ◽  
The Impact

scholarly journals Joint optimization of network selection and task offloading for vehicular edge computing

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Access Point ◽  
Edge Computing ◽  
Network Selection ◽  
Network Access ◽  
Task Execution ◽  
Point Selection ◽  
Vehicle Movement ◽  
Task Offloading

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

scholarly journals Joint Impacts of Drought and Habitat Fragmentation on Native Bee Assemblages in a California Biodiversity Hotspot

Insects ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 135
Author(s):  
Keng-Lou James Hung ◽  
Sara S. Sandoval ◽  
John S. Ascher ◽  
David A. Holway
Keyword(s):  
Habitat Fragmentation ◽  
Large Scale ◽  
Global Climate ◽  
Relative Magnitude ◽  
Urban Environments ◽  
Floral Resources ◽  
Severe Drought ◽  
Drought Event ◽  
Fragmented Habitat ◽  
Native Bee

Global climate change is causing more frequent and severe droughts, which could have serious repercussions for the maintenance of biodiversity. Here, we compare native bee assemblages collected via bowl traps before and after a severe drought event in 2014 in San Diego, California, and examine the relative magnitude of impacts from drought in fragmented habitat patches versus unfragmented natural reserves. Bee richness and diversity were higher in assemblages surveyed before the drought compared to those surveyed after the drought. However, bees belonging to the Lasioglossum subgenus Dialictus increased in abundance after the drought, driving increased representation by small-bodied, primitively eusocial, and generalist bees in post-drought assemblages. Conversely, among non-Dialictus bees, post-drought years were associated with decreased abundance and reduced representation by eusocial species. Drought effects were consistently greater in reserves, which supported more bee species, than in fragments, suggesting that fragmentation either had redundant impacts with drought, or ameliorated effects of drought by enhancing bees’ access to floral resources in irrigated urban environments. Shifts in assemblage composition associated with drought were three times greater compared to those associated with habitat fragmentation, highlighting the importance of understanding the impacts of large-scale climatic events relative to those associated with land use change.

An Evaluation of Pseudonym Changes for Vehicular Networks in Large-Scale, Realistic Traffic Scenarios

2018 ◽  
Vol 19 (10) ◽  
pp. 3400-3405 ◽  
Author(s):  
David Forster ◽  
Hans Lohr ◽  
Anne Gratz ◽  
Jonathan Petit ◽  
Frank Kargl
Keyword(s):  
Large Scale ◽  
Vehicular Networks

scholarly journals Accurate Real-Time Traffic Speed Estimation Using Infrastructure-Free Vehicular Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Zongjian He ◽  
Buyang Cao ◽  
Yan Liu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Vehicular Networks ◽  
Route Planning ◽  
Speed Estimation ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Traffic Trace ◽  
Traffic Speed ◽  
Traffic Speed Estimation

Real-time traffic speed is indispensable for many ITS applications, such as traffic-aware route planning and eco-driving advisory system. Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed. However, this assumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world. In this paper, we propose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks. The proposed solution utilizes macroscopic traffic flow model to estimate the traffic condition. The selected model only relies on vehicle density, which is less likely to be affected by the traffic dynamicity. In addition, we also demonstrate an application of the proposed solution in real-time route planning applications. Extensive evaluations using both traffic trace based large scale simulation and testbed based implementation have been performed. The results show that our solution outperforms some existing ones in terms of accuracy and efficiency in traffic-aware route planning applications.

Large Scale Cognitive Wireless Networks

2013 ◽  
pp. 26-52
Author(s):  
Liang Song ◽  
Petros Spachos ◽  
Dimitrios Hatzinakos
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Cognitive Radio ◽  
Data Aggregation ◽  
Network Architecture ◽  
Large Scale ◽  
Vehicular Networks ◽  
Network Resource ◽  
Cognitive Wireless Networks ◽  
Definition Of

Cognitive radio has been proposed to have spectrum agility (or opportunistic spectrum access). In this chapter, the authors introduce the extended network architecture of cognitive radio network, which accesses not only spectrum resource but also wireless stations (networking nodes) and high-level application data opportunistically: the large-scale cognitive wireless networks. The developed network architecture is based upon a re-definition of wireless linkage: as functional abstraction of proximity communications among wireless stations. The operation spectrum and participating stations of such abstract wireless links are opportunistically decided based on their instantaneous availability. It is able to maximize wireless network resource utilization and achieve much higher performance in large-scale wireless networks, where the networking environment can change fast (usually in millisecond level) in terms of spectrum and wireless station availability. The authors further introduce opportunistic routing and opportunistic data aggregation under the developed network architecture, which results in an implementation of cognitive unicast and cognitive data-aggregation wireless-link modules. In both works, it is shown that network performance and energy efficiency can improve with network scale (such as including station density). The applications of large-scale cognitive wireless networks are further discussed in new (and smart) beyond-3G wireless infrastructures, including for example real-time wireless sensor networks, indoor/underground wireless tracking networks, broadband wireless networks, smart grid and utility networks, smart vehicular networks, and emergency networks. In all such applications, the cognitive wireless networks can provide the most cost-effective wireless bandwidth and the best energy efficiency.

The Use of Social Media for Urban Planning

2019 ◽  
pp. 1049-1070
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

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