TIHOO: An Enhanced Hybrid Routing Protocol in Vehicular Ad-hoc Networks

Yahiabadi, Shirin Rahnamaei; Barekatain, Behrang; Raahemifar, Kaamran

doi:10.1186/s13638-019-1503-4

Cited by 25 publications

(12 citation statements)

References 42 publications

(70 reference statements)

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“…The performance of ACARP protocol is measured in five performance metrics such as average throughput, communication delay (latency), PDR, communication overhead, and CO2 emission. These parameters are evaluated compared to two recent fuzzy-logic based route discovery and congestion aware routing methods for VANETS such as TIHOO [34] and FBVANET [35]. The main reasons for selecting these two protocols for comparative study are (1) recently proposed protocols for VANET, (2) congest-aware routing, (3) fuzzy-logic approach to discover the reliable routes by detecting the congestions.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The re-routing process is not adaptive and time-consuming with such an approach. The TIHOO routing solution was proposed in [34] for VANET using the cuckoo and improved fuzzy algorithms to address the problems of discovering stable and congestion-free paths. They used the vehicle speed, its moving direction, and geographical distance from the destination node as an input to the fuzzy logic system.…”

Section: A Congestion Detection and Controlmentioning

confidence: 99%

“…The properties like high message rates, unreliable channel quality, and severe mobility lead to a significant problem for VANETs routing. The congestion control protocols [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] were designed to make sure the reliable transmission of security messages in V2V communications. However, adaptive congestion prediction and its utilization during the route discovery are missing in all works.…”

Section: B Research Motivation and Contributionsmentioning

confidence: 99%

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Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) using Type-2 Fuzzy Model to Establish Reliable Routes

Giripunje

Vidyarthi

Shandilya³

2021

Preprint

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In urban areas, a significant increase in vehicles day-by-day leads to challenges like accidents and pollution such as air and noise. The emission of Carbon dioxide (CO2) from vehicles leads to air pollution. The major cause of increased emission is traffic conditions in urban areas. Thus, Quality of Service (QoS) becomes a very challenging research problem by considering eco-friendly and reliable transportation. The appropriate congestion or traffic situation management techniques reduce the possibilities of accidents and pollution. The congestion control methods should take into account the properties such as fairness, decentralization, network characteristics, and application demands in VANET. The current methods failed to address all such properties and trade-offs for VANET communications. This paper proposed Adaptive Congestion Aware Routing Protocol (ACARP) for VANET using the dynamical artificial intelligence (AI) technique. In ACARP, the adaptive congestion detection algorithm is designed using the type-2 fuzzy logic AI technique. The fuzzy model builds to detect the congestion around each vehicle using three fuzzy-inputs such as bandwidth occupation, link quality, and moving speed. Using three parameters, the fuzzy rules are designed in the first phase. In the second phase, the inferences model introduced where the fuzzy decision has been made. In the last phase, defuzzifuzification is applied and the congestion probability estimated in the range of 0-1 for each vehicle. Then the status of congestion detection updated using the pre-defined threshold value for each vehicle. The status of congestion and its probability values have been utilized to establish safe and reliable routes for data transmission. It saves significant communication overhead and hence CO2 emissions in the network. The simulation results prove that the planned protocol improved the QoS presentation and significant CO2 emission compared to underlying fuzzy-based methods.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: A Congestion Detection and Controlmentioning

confidence: 99%

Section: B Research Motivation and Contributionsmentioning

confidence: 99%

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Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) using Type-2 Fuzzy Model to Establish Reliable Routes

Giripunje

Vidyarthi

Shandilya³

2021

Preprint

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“…But, they have not considered the link quality factor, which is the most effective parameter to increase the efficiency of routing. In TIHOO [35], the source and other neighbor vehicles are broadcasting the limited number of Route Request Packet (RREQ) using the concept of fuzzy logic. This minimizes the number of extra packets other than the actual data packet during the data communication.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy logic-based VANET routing method to increase the QoS by considering the dynamic nature of vehicles

et al. 2021

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Vehicular ad hoc network usually operates in various challenging situations like frequent topology changes, high vehicular mobility and the wide range of communication networks. Due to this it is very hard to maintain a higher data rate and also to achieve low latency during data communication. To overcome these problems, given the dynamic natures of all the vehicles in a given network in the proposed routing method, we have defined two fundamental parameters to determine the forwarding vehicle. The first parameter, which we developed, we call it "Channel quality factor (CQF)" or 'Z'. The other parameter known as "Communication expiration time" or 'T' together with CQF is used in the present method to determine the forwarding vehicle. Fuzzy logic is also used to optimize various Quality of Service matrices. This proposed routing method involves two main parts; one is for forwarding Vehicle selection in the road based on the fuzzy logic. The second one is Road selection at the Road Junction to select the right path to reach the signal to the destination vehicle. The simulation results show that our proposed method performs well compare to other well-known protocols (MoZo, BRAVE, OFAODV) in terms of the average end to end delay, packet delivery ratio and control packet overhead, given any number of vehicles in a set of streets. While we are comparing with VEFR protocol, our proposed method shows higher performance in terms of average E2E delay and control packet overhead. However, it is interesting to see that VEFR gives ∼ 5% better result than our proposed method when the number of vehicles in the streets are lower. But in the limit, when the number of vehicles reaches close to ∼ 1900 the difference between the proposed method and method in VEFR goes to zero. At last we compare our proposed method with junction based two V2I protocols. In every cases, it shows better result even though we change the speed of the vehicles, beacon interval, channel data rate and transmission region.

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“…Hence, the ANN must be operated continuously, and a small change in the path leads to increased packet drop. A hybrid intelligent routing method was designed by combining an improved fuzzy and cuckoo approach [41]. Based on the fitness function, the optimal route was chosen.…”

Section: Related Workmentioning

confidence: 99%

Stabilized Clustering Enabled V2V Communication in an NDN-SDVN Environment for Content Retrieval

et al. 2020

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Content retrieval is becoming an emerging application on an integrated network structure referred to as Software Defined Network and Vehicular Ad hoc Network (SDVN). The SDVN is expected to flexibly provide efficient packet routing without deploying road side units. In such the infrastructure-less environment, however, it is challenging to retrieve contents from dynamic networks clustered with vehicles on roadways. As a solution to the problem, we propose a new network design by conjoining Named Data Network (NDN) with SDVN (NDN-SDVN). The new NDN-SDVN is designed as to improve stableness of the cluster formation and hence enables to efficiently perform the content retrieval over Vehicle-to-Vehicle (V2V) communications. Note that one of the most important problems in the V2V is how to select a cluster head. The proposed NDN-SDN adaptively determines the cluster head by using the SDN controller under the maximum Quality of Service (QoS) criterion, while reserving an assistance head based on a Moth Flame Optimization (MFO) algorithm to immediately recompose a new cluster with the back-off time. Moreover, the content retrieval in the NDN-SDVN can be performed over both intra-and inter-cluster communications. To this end, a Kalman filter with Neural Network (KF-NN) is applied for selecting a certain number of preferable vehicles in 1-hop in order to mitigate the broadcast storm problem that often occurs in an NDN. Content retrieval is initiated by sending interest packets including two additional fields of the timestamp and the target MAC address. The head vehicle of the SDN controller maintains ontology regarding the content information cashed by other vehicles, which significantly improves the packet delivery ratio. This NDN-SDVN design is developed on the OMNeT++ simulator and their advantageous results are evaluated in terms of the head lifetime, the cluster lifetime, the satisfactory rate, the latency and the packet delivery ratio.

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TIHOO: An Enhanced Hybrid Routing Protocol in Vehicular Ad-hoc Networks

Cited by 25 publications

References 42 publications

Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) using Type-2 Fuzzy Model to Establish Reliable Routes

Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) using Type-2 Fuzzy Model to Establish Reliable Routes

Fuzzy logic-based VANET routing method to increase the QoS by considering the dynamic nature of vehicles

Stabilized Clustering Enabled V2V Communication in an NDN-SDVN Environment for Content Retrieval

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