Abstract:Summary
Vehicular ad hoc networks (VANETs) are emergent concepts in terms of infrastructure‐less communication. The data dissemination is usually done using broadcast schemes. Data broadcast in VANETs is a challenging issue due to the high mobility vehicles and the varying density. On one hand, these vehicles have to share and disseminate the safety‐critical information, in real time, to other intended vehicles. On the other hand, the existing broadcast solutions do not succeed, till now, to fulfill VANETs req… Show more
“…It is the measure of the source node detecting possible number of vehicles in the surrounding ( NN AVAIL_NLOS ) that are highly matching with the requirements of their range of communication. This metric is highly essential in NLOS contexts where failures of packet delivery to the interacting vehicles are possible due to the presence of obstacles 32 . It can be also defined as the potential of the proposed NLOS node detection approach in successfully detecting the NLOS situations ( NN ACTUAL_NLOS ) in the network.…”
Section: Simulation Results and Discussionmentioning
confidence: 99%
“…This metric is highly essential in NLOS contexts where failures of packet delivery to the interacting vehicles are possible due to the presence of obstacles. 32 It can be also defined as the potential of the proposed NLOS node detection approach in successfully detecting the NLOS situations (NN ACTUAL_NLOS ) in the network. Maximum number of vehicles 400…”
In emergency situations, the localization of Non Line of Sight (NLOS) nodes is essential for enhancing the reaction time of the vehicle drivers in response to the stimulus generated in VANETs. However, the existence of NLOS nodes intentionally or intentionally introduces broadcasting storm and congestion in the network. In this paper, a Hybrid Invasive Weed Optimization and Squirrel Search Algorithm-Localization Mechanism (HIWO-SSA-LM) is proposed for improving the localization of NLOS nodes to enhance the reliable data dissemination under emergency situations in VANETs. This proposed HIWO-SSA-LM integrates the reproductive capability of Invasive Weeds into the reproductive potential of a Flying Squirrel Algorithm for sustaining the degree of balance under exploitation and exploration involved during NLOS node localization. The predominance of the proposed HIWO-SSA-LM is explored based on experiments conducted using EstiNet 8.1 simulator using emergency message delivery rate, channel utilization rate, and neighborhood awareness rate by varying the number of vehicles and NLOS nodes in the network.
“…It is the measure of the source node detecting possible number of vehicles in the surrounding ( NN AVAIL_NLOS ) that are highly matching with the requirements of their range of communication. This metric is highly essential in NLOS contexts where failures of packet delivery to the interacting vehicles are possible due to the presence of obstacles 32 . It can be also defined as the potential of the proposed NLOS node detection approach in successfully detecting the NLOS situations ( NN ACTUAL_NLOS ) in the network.…”
Section: Simulation Results and Discussionmentioning
confidence: 99%
“…This metric is highly essential in NLOS contexts where failures of packet delivery to the interacting vehicles are possible due to the presence of obstacles. 32 It can be also defined as the potential of the proposed NLOS node detection approach in successfully detecting the NLOS situations (NN ACTUAL_NLOS ) in the network. Maximum number of vehicles 400…”
In emergency situations, the localization of Non Line of Sight (NLOS) nodes is essential for enhancing the reaction time of the vehicle drivers in response to the stimulus generated in VANETs. However, the existence of NLOS nodes intentionally or intentionally introduces broadcasting storm and congestion in the network. In this paper, a Hybrid Invasive Weed Optimization and Squirrel Search Algorithm-Localization Mechanism (HIWO-SSA-LM) is proposed for improving the localization of NLOS nodes to enhance the reliable data dissemination under emergency situations in VANETs. This proposed HIWO-SSA-LM integrates the reproductive capability of Invasive Weeds into the reproductive potential of a Flying Squirrel Algorithm for sustaining the degree of balance under exploitation and exploration involved during NLOS node localization. The predominance of the proposed HIWO-SSA-LM is explored based on experiments conducted using EstiNet 8.1 simulator using emergency message delivery rate, channel utilization rate, and neighborhood awareness rate by varying the number of vehicles and NLOS nodes in the network.
“…For the comparative study, we choose three other methods: eKMPR [1], FBBPA [26] and ASPBT [27]. The eKMPR technique is a statistical approach for estimating vehicle mobility.…”
Section: ) Taxonomy Of the Compared Techniquesmentioning
confidence: 99%
“…VANETs (vehicular ad hoc networks) enable a wide range of intelligent transportation system services. Vehicle-to-vehicle and vehicle-to-infrastructure communications are used to provide services varying from road safety to infotainment and traffic management [1]. VANET creates self-organizing networks composed of nodes with dedicated short-range communication (DSRC) implanted in automobiles.…”
VANETs (Vehicular Ad hoc NETworks) are considered among the world's largest networks. These networks are providing multiple services like infotainment applications, safety services, driver assistance, and even video on demand. On one hand, VANETs are characterized by their random topology and dynamic behavior that varies in urban context, and which highly changes in highways. On the other hand, diffusing information is a fundamental task to deliver multiple services. Thus, the broadcasting task is a challenging problem and need more investigation. In fact, to achieve this task, artificial intelligence and learning based computing seem to be one of the most appropriate options that best fits the dynamic behavior of VANETs. Accordingly, in this paper we propose a novel hybrid relay selection technique to perform the broadcasting task based on a reinforcement learning method. Our proposition is initially to combine an artificial neural network-based classification applied to select forwarding nodes, and in the second phase, we apply the Viterbi algorithm as a reinforcement tool to refine the first classification. To measure the performance of our contribution, we adopt a grid map scenario with varied traffic densities. Afterwards, we analyze and compare the simulation results with other methods in the literature based on different parameters such as the success rate, the data loss, the saved rebroadcasts, and the delay. We conclude by proving that the proposed technique combining deep learning along with reinforcement learning outperforms other recently proposed broadcasting schemes based on the results which show that the new solution increased the success rate by 16%, the saved rebroadcasts by 20%, and reduced the delay by 23%.
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