SOFCluster: Safety‐oriented, fuzzy logic‐based clustering scheme for vehicular ad hoc networks

Aissa, Mohamed; Bouhdid, Badia; Mnaouer, Adel Ben; Belghith, Abdelfettah; Al-Ahmadi, Saad

doi:10.1002/ett.3951

Cited by 11 publications

(9 citation statements)

References 24 publications

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“…The simulation experiments of the proposed BCOA‐NCT and the benchmarked CAMONET, GOACT, and CACONET approaches are conducted using Matlab version 8.5.0 installed over the system with configuration of core i5 2.2 GHz processor and the capacity of 4 GB RAM 30–34 . The complete experiments are conducted with four different road segment sizes ranging from 1 km × 1 km to 4 km × 4 km of network size.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The simulation experiments of the proposed BCOA-NCT and the benchmarked CAMONET, GOACT, and CACONET approaches are conducted using Matlab version 8.5.0 installed over the system with configuration of core i5 2.2 GHz processor and the capacity of 4 GB RAM. [30][31][32][33][34] The complete experiments are conducted with four different road segment sizes ranging from 1 km × 1 km to 4 km × 4 km of network size. The number of vehicular nodes used for simulation varies from 10 to 60 with the range of transmission being adjusted from 100 to 600 m. The velocity of the vehicular nodes is dynamically changed from 80 to 120 km/h with the nodes moving in a bidirectional way along the x-axis.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Border Collie optimization algorithm‐based node clustering technique in vehicular ad hoc networks

Godi¹,

Janakiraman²

2022

Trans Emerging Tel Tech

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In vehicular ad hoc networks (VANETs), the nodes are vehicles and these vehicles communicate with one another on the road. The vehicular nodes on the road move continuously and it introduces dynamic changes in the topology of the network. When the node density in the vehicular network is high, then it is still more challenging as these situations introduce different challenges for network scalability and hurdles the process of determining an optimal route in the vehicular network. At this juncture, clustering protocols are often utilized for resolving the issue of network scalability and the factors that hinders process of identifying optimal routes in vehicular network. In this article, Border Collie optimization algorithm‐based node clustering technique (BCOA‐NCT) is proposed for achieving optimal cluster head selection that minimizes network overhead in uncertain node density situations. This BCOA‐NCT is proposed for facilitating equal concentration on both exploitation and exploration during the process of clustering. It utilized the strategy of eyeing for preventing the algorithm from being stuck into local optima during the cluster head selection process. The simulation experiments of the proposed BCOA‐NCT and the benchmarked schemes confirmed better throughput of 6.19%, minimized end‐to‐end delay of 7.24%, reduced mean energy consumptions of 6.89%, and reduction in the number of beacon messages utilized by 8.26% under the impact of different density of vehicular node. The outcome of the results indicated that the proposed BCOA‐NCT on par with the baseline approaches is capable enough in handling the issue of scalability and better estimation of optimal routes under the increase of node density in the vehicular network.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Border Collie optimization algorithm‐based node clustering technique in vehicular ad hoc networks

Godi¹,

Janakiraman²

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Aissa et al [29] proposed a novel strategy for constructing fuzzy logic-based clustering algorithms useful for VANETs. The CH election was performed by taking into account the candidate nodes' relative speed and distance to other members of the cluster, using a fuzzy logic inference system.…”

Section: Related Workmentioning

confidence: 99%

EMASS: A Novel Energy, Safety and Mobility Aware-Based Clustering Algorithm for FANETs

2021

Self Cite

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The Unmanned Aerial Vehicles (UAVs), organized as a Flying Ad-hoc NETwork (FANET), are used to make effective remote monitoring in diverse applications. Due to their high mobility, their energy consumption is increasingly affected leading to reduced network stability and communication efficiency. The design of node clustering of a FANET needs to consider the number of UAVs in the vicinity (transmission range) in order to ensure an adaptive reliable routing. Novel clustering schemes have been employed to deal with the highly dynamic flying behavior of UAVs and to maintain network stability. In this context, a new clustering algorithm is proposed to address the fast mobility of UAVs and provide safe inter-UAV distance, stable communication and extended network lifetime. The main contributions of this paper are first to extend and improve important metrics used in two well-known algorithms in the literature namely: The Bio-Inspired Clustering Scheme for FANETs (BICSF) and the Energy Aware Link-based Clustering (EALC). Then, exploiting the improved metrics, an Energy and Mobility-aware Stable and Safe Clustering (EMASS) algorithm, built upon new schemes useful for ensuring stability and safety in FANETs, is proposed. The simulation results showed that the EMASS algorithm outperformed the BICSF and the EALC algorithms in terms of better cluster stability, guaranteed safety, higher packet deliverability, improved energy saving and lower delays.

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“…Aissa et al [25] proposed a new dissemination scheme based on a distributed clustering algorithm. In this scheme, the cluster heads are elected using fuzzy logic combining multiple attributes.…”

Section: Dsrc-based Data Disseminationmentioning

confidence: 99%

Towards Optimal Dissemination of Emergency Messages in Internet of Vehicles: A Dynamic Clustering-Based Approach

Azzaoui

Korichi

Brik³

et al. 2021

Electronics

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In this paper, we target dissemination issues of emergency messages in a highly dynamic Internet of Vehicles (IoV) network. IoV is emerging as a new class of vehicular networks to optimize road safety as well as users’ comfort. In such a context, forwarding emergency messages through vehicle-to-vehicle communications (V2V) plays a vital role in enabling road safety-related applications. For instance, when an accident occurs, forwarding such information in real time will help to avoid other accidents in addition to avoiding congestion of network traffic. Thus, dissemination of emergency information is a major concern. However, on the one hand, vehicle density has increased in the last decade which may lead to several issues including message collisions, broadcast storm, and the problem of hidden nodes. On the other hand, high mobility of vehicles and hence dynamic changes of network topology result in failure of dissemination of emergency packets. To overcome these problems, we propose a new dissemination scheme of emergency packets by vehicles equipped with both DSRC and cellular LTE wireless communication capabilities. Our scheme is based on a dynamic clustering strategy, which includes a new cluster head selection algorithm to deal with the broadcast storm problem. Furthermore, our selection algorithm enables not only the election of the most stable vehicles as cluster heads, and hence their exploitation in forwarding the emergency information, but also the avoidance of packet collisions. We simulated our scheme in an urban environment and compared it with other data dissemination schemes. Obtained results show the efficiency of our scheme in minimizing collision and broadcast storm problems, while improving latency, packet delivery ratio and data throughput, as compared to other schemes.

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SOFCluster: Safety‐oriented, fuzzy logic‐based clustering scheme for vehicular ad hoc networks

Cited by 11 publications

References 24 publications

Border Collie optimization algorithm‐based node clustering technique in vehicular ad hoc networks

Border Collie optimization algorithm‐based node clustering technique in vehicular ad hoc networks

EMASS: A Novel Energy, Safety and Mobility Aware-Based Clustering Algorithm for FANETs

Towards Optimal Dissemination of Emergency Messages in Internet of Vehicles: A Dynamic Clustering-Based Approach

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