A Fast Reroute Algorithm for Infotainment Service in Internet of Vehicles

Abstract

Failure recovery in Internet of Vehicles (IoVs) is critical to high quality service provisioning. The main challenge is how to achieve fast rerouting without introducing high complexity and resource usage due to the dynamic topology and the constraints on bandwidth. In this paper, we propose a traffic prediction-based fast reroute algorithm for use among the vehicles in IoVs. The proposed algorithm uses the Wavelet Neural Network (WNN) model to predict a vehicle's network traffic. When the predicted value is greater than the predefined network traffic threshold, both Adaptive Retransmission Trigger (ART) that contributes to switch to a better alternate path in advance and trigger efficient retransmission behaviors are enabled successively. Performance comparison of our proposed algorithm with Ant-based Delay-Sensitive Vehicular Routing (AntVehiNet) shows that WNNPFR can: (a) maximize the service data delivery rate by load balancing, (b) provide high quality of service delivery for multimedia streams by switching to a better path towards a target node in advance, (c) reduce useless data retransmissions when various network failures occur, and (d) maintain lower routing overhead.

Efficient and scalable execution of smart city parallel applications

“…[12][13][14] The analysis of Internet traffic generated by mobile users is an important application scenario today, 16,17,32,33 as numerous vehicles possess powerful sensing, networking, communication, and data-processing capabilities and can exchange information with each other (vehicle to vehicle, V2V) or with the roadside infrastructure such as camera and street lights (vehicle to infrastructure, V2I) over various protocols, including HTTP, SMTP, TCP/IP, WAP, and Next Generation Telematics Protocol (NGTP). 16,17,34,35 A first example concerns to the so-called infotainment applications, such as community services (ie, point-of-interest notifications, electronic and financial services, media downloading, and parking zone management), which can require the transmission of a huge amount of data among users and road units for an efficient provisioning of such services. 16,17,34,35 A first example concerns to the so-called infotainment applications, such as community services (ie, point-of-interest notifications, electronic and financial services, media downloading, and parking zone management), which can require the transmission of a huge amount of data among users and road units for an efficient provisioning of such services.…”

“…15 The rapid and correct prediction of Internet data traffic has several applicative impacts in smart city scenarios. 34 A second application, recently described in Altomare et al, 36 is the accurate prediction of Internet traffic, which is useful to anticipate possible bottlenecks in some portions of the avenue, and save energy and batteries consumption by dynamically redistributing the workload between fixed and mobile devices. 34 A second application, recently described in Altomare et al, 36 is the accurate prediction of Internet traffic, which is useful to anticipate possible bottlenecks in some portions of the avenue, and save energy and batteries consumption by dynamically redistributing the workload between fixed and mobile devices.…”

“…16,17,34,35 A first example concerns to the so-called infotainment applications, such as community services (ie, point-of-interest notifications, electronic and financial services, media downloading, and parking zone management), which can require the transmission of a huge amount of data among users and road units for an efficient provisioning of such services. 34 A second application, recently described in Altomare et al, 36 is the accurate prediction of Internet traffic, which is useful to anticipate possible bottlenecks in some portions of the avenue, and save energy and batteries consumption by dynamically redistributing the workload between fixed and mobile devices. 35 Finally, usage pattern prediction of requests can be also used to influence the admission/denial of service demands made by priority and nonpriority users, in order to match their respective Quality of Service agreements.…”

“…The rapid and correct prediction of Internet data traffic has several applicative impacts in smart city scenarios. () A first example concerns to the so‐called infotainment applications , such as community services (ie, point‐of‐interest notifications, electronic and financial services, media downloading, and parking zone management), which can require the transmission of a huge amount of data among users and road units for an efficient provisioning of such services . A second application, recently described in Altomare et al, is the accurate prediction of Internet traffic, which is useful to anticipate possible bottlenecks in some portions of the avenue, and save energy and batteries consumption by dynamically redistributing the workload between fixed and mobile devices .…”

A Levy Flight Sine Cosine Algorithm for Global Optimization Problems

“…In recent years, the complex real-world problems have been solved by many swarm intelligence algorithms (Li et al, 2016;Miloud et al, 2019;Ghosh et al, 2018). Compared with traditional optimization methods, the swarm intelligence algorithm can effectively and conveniently solve optimizationproblemsduetoitsadvantagesstrongrobustness,andabilityofeasyimplementation (Liuetal.,2020;Lietal.,2017).Theswarmintelligencealgorithmsimulatesvariousnaturebehaviors toresearchglobaloptimumsolution,likeantcolonyoptimizationalgorithm(ACO) (Dorigoetal., 1996) simulates behavior of ants; particle swarm optimization algorithm (PSO) (Kennedy et al, 1995;Eberhartetal.,2002)isbasedonthebehaviorofaparticlelikeabird.And,variousnewswarm intelligencealgorithmshavebeenappearedinthepastfewyears.Forexample,batalgorithm(BA) (Yang,2010),artificialbeecolonyalgorithm(ABC) (Karabogaetal.,2007),cuckooalgorithm(CS) (Yang&Deb,2009),differentialevolutionalgorithm(DE) (Alietal.,2015),greywolfoptimizer algorithm(GWO) (Mirjalilietal.,2014),sinecosinealgorithm(SCA) (Mirjalili,2016),polarbear optimization algorithm (PBO) (Polap & Niak, 2017), etc.…”