ACO and PSO Algorithms for Developing a New Communication Model for VANET Applications in Smart Cities

Hernafi, Yassine; Mohamed, Ben Ahmed; Bouhorma, Mohammed

doi:10.1007/s11277-017-4286-0

Cited by 24 publications

(10 citation statements)

References 21 publications

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“…Furthermore, special attention is required on a collection of original security methods to attain safe certificate revocation as well as the preservation of privacy. [17][18][19] The routing and security have a crucial role in the analysis, research, and engineering of the VANET. The significance of an efficient routing scheme is to obtain the optimal shortest path for communication from one node to the other.…”

Section: Related Workmentioning

confidence: 99%

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Ragavan¹,

Venkatalakshmi²,

Vijayalakshmi³

2020

Computational Intelligence

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Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV-based route selection method provides better performance.

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Section: Related Workmentioning

confidence: 99%

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Ragavan¹,

Venkatalakshmi²,

Vijayalakshmi³

2020

Computational Intelligence

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“…This factor can be changed in a range of [1-MaxIteration ants ]. By our experiment, if the DP CS value has changed in the range of [1][2][3][4][5][6][7][8][9][10], the convergence speed of the algorithm for finding the best solution becomes quicker. On the other hand, increasing this value by more than 10 reduces accuracy for finding the best solution by ants.…”

Section: (I) Dynamic Pheromone Effectiveness (Dpe)mentioning

confidence: 99%

“…Such principles can be deduced from bird behavior, genes, and insect behavior. In this context, we can name some well-known algorithm approaches for VANET clustering, such as ACO-based [7,8], the Grey Wolf Optimizer (GWO) [9], and Particle Swarm Optimization (PSO) [10]. To be precise, most of the research in this area is done based on a single-objective problem, learning weights and static ranges of transmission, except for Dragon Fly Algorithm [11].This study focuses on answering some important questions about routing in a wireless mesh network that are listed below.…”

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confidence: 99%

Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)

2019

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The Internet of Vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. IoV is vehicle communications, which is also a part of the Internet of Things (IoT). Continuous topological changes of vehicular communications are a significant issue in IoV that can affect the change in network scalability, and the shortest routing path. Therefore, organizing efficient and reliable intercommunication routes between vehicular nodes, based on conditions of traffic density is an increasingly challenging issue. For such issues, clustering is one of the solutions, among other routing protocols, such as geocast, topology, and position-based routing. This paper focuses mainly on the scalability and the stability of the topology of IoV. In this study, a novel intelligent system-based algorithm is proposed (CACOIOV), which stabilizes topology by using a metaheuristic clustering algorithm based on the enhancement of Ant Colony Optimization (ACO) in two distinct stages for packet route optimization. Another algorithm, called mobility Dynamic Aware Transmission Range on Local traffic Density (DA-TRLD), is employed together with CACOIOV for the adaptation of transmission range regarding of density in local traffic. The results presented through NS-2 simulations show that the new protocol is superior to both Ad hoc On-demand Distance Vector (AODV) routing and (ACO) protocols based on evaluating routing performance in terms of throughput, packet delivery, and drop ratio, cluster numbers, and average end-to-end delay.

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“…Looking into how PSO was explored in VANET, the work by Hernafi et al proposed and investigated a network selection algorithm inspired by PSO in terms of the geographic position of the vehicle and a shortest route selection method based on the ant colony [33]. The focus was on the vertical handover technique with the consideration of RSSI and the effect of the number of cars.…”

Section: Previous Work On Pso In Vanetmentioning

confidence: 99%

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2019

IJWMN

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One of the most important factors to implement VANET is by considering the variety of wireless networks available around the city as well as the vehicles traffic scenarios. However, by providing a diverse range of wireless access technologies, it is necessary to provide continuous network connectivity as well as selecting the most suitable network technology and performance. Many researchers have worked on building algorithms for selecting the best network to improve the handover process. However, with high-speed vehicles mobility, the vertical handover process became the most challenging task in order to achieve realtime network selection. This paper proposes a bio-inspired network selection algorithm influenced by insect's behaviour which combines Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). The proposed algorithm is applied to process multi-criteria parameters to evaluate the best available network and then execute the handover process seamlessly. The results demonstrate the benefits of the proposed Multi-Criteria ABC-PSO method by reducing the handover decision delays by 25%. It gives the optimum performance in terms of network selections and reduces the handover latency by 14.5%. The proposed algorithm also reduces the number of unnecessary handovers by 48% for three different mobility scenarios based on traffic environments (highway, urban and traffic jam).2 selecting the best among them. Hence, the concept of mobility in a heterogeneous wireless network implies the continuation of data transmission or maintaining an ongoing session while the point of attachment changes during VHO. The VHO happens when a Mobile Node (MN) roams between different networks and change the device interface (physical layer) which leads to different direction of data session in the user side as well as the network attachment point in the network side (network layer).Our proposed schemes present a multi-criteria artificial bee colony hybrid with particle swarm optimisation algorithm for evaluating the information gathered from the mobile nodes in the handover. The algorithm will process and calculate the required handover timing for each node in advance, based on the mobile nodes' velocity and received signal strength to reduce the handover process latency. The information is used to support all vertical handover operations, which includes network discovery, handover decision, and handover implementation.In the initial stage of the operation, the previous Access Point (AP) provides information about the neighbouring networks for the MN to quickly discover available networks. The second stage of the operation is selecting the best available network for the MN by considering its velocity and distance. The final operation of the proposed scheme involves the mobile node sharing the current information with its neighbouring nodes based on its current location. The schemes run on realtime simulation based on three mobility scenarios in city environment which are highway, urban and traffic jam scenario.

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ACO and PSO Algorithms for Developing a New Communication Model for VANET Applications in Smart Cities

Cited by 24 publications

References 21 publications

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)

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