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The vehicular ad hoc network (VANET) is an emerging area for smart cities as observed in last few decades. However, some hurdles for VANET exist that need to be resolved before its full implementation in smart cities. Routing is one of the main factors for having effective communication between smart vehicles that urgently needs to be addressed. One factor that affects communication between the vehicles is the intersection points that obstruct the communication. The paper aims to discuss these issues.

The conventional routing schemes fail to address the intersection problems that occur during the two points of communication. Therefore, this paper analyses the performance of existing position-based routing protocol for inter-vehicle ad hoc networks, considering the impact of a number of intersections. This simulation evaluates different position-based routing protocols such as Intersection-based Distance and Traffic-Aware Routing (IDTAR), Greedy Traffic-Aware Routing, Anchor-based Street and Traffic-Aware Routing and Geographic Source Routing, based on road topology and the number of intersections.

As a result, the protocol IDTAR has a lower end-to-end delay and high packet delivery ratio in terms of the number of intersections as a case study of smart cities. This concludes that IDTAR can be adaptive to smart cities communication, although some questions need to be considered in terms of its security, compatibility, reliability and robustness.

The role of VANET has been highlighted in smart cities due to its implications in day-to-day life. The vehicles in VANET are equipped with wireless communication nodes to provide network connectivity. Such types of network operate without the legacy infrastructure, as well as legacy client/servers.

Additionally, the study contributes to smart cities by measuring the performance of position-based routing protocols for VANETs.

This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.

The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance.

A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead.

The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks.

This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

In vehicular ad hoc networks (VANETs), the information transmitted is broadcast in a free access environment. Therefore, VANETs are vulnerable against attacks that can directly perturb the performance of the networks and then provoke big fall of capability. Black hole attack is an example such attack, where the attacker node pretends that having the shortest path to the destination node and then drops the packets. This paper aims to present a new method to detect the black hole attack in real-time in a VANET network.

This method is based on capability indicators that are widely used in industrial production processes. If the different capability indicators are greater than 1.33 and the stability ratio (S_r) is greater than 75%, the network is stable and the vehicles are communicating in an environment without the black hole attack. When the malicious nodes representing the black hole attacks are activated one by one, the fall of capability becomes more visible and the network is unstable, out of control and unmanaged, due to the presence of the attacks. The simulations were conducted using NS-3 for the network simulation and simulation of urban mobility for generating the mobility model.

The proposed mechanism does not impose significant overheads or extensive modifications in the standard Institute of Electrical and Electronics Engineers 802.11p or in the routing protocols. In addition, it can be implemented at any receiving node which allows identifying malicious nodes in real-time. The simulation results demonstrated the effectiveness of proposed scheme to detect the impact of the attack very early, especially with the use of the short-term capability indicators (Cp, Cpk and Cpm) of each performance metrics (throughput and packet loss ratio), which are more efficient at detecting quickly and very early the small deviations over a very short time. This study also calculated another indicator of network stability which is S_r, which allows to make a final decision if the network is under control and that the vehicles are communicating in an environment without the black hole attack.

According to the best of the authors’ knowledge, the method, using capability indicators for detecting the black hole attack in VANETs, has not been presented previously in the literature.

This research specifies the factors impacting on the success of supply chain management (SCM) systems in the organizations. This paper aims to assess the effect of knowledge sharing, the vehicular ad hoc network (VANET), radio frequency identification technology (RFID) and near field communications (NFC) and the social capabilities of information technology (IT) and information and communication technology (ICT)on the success of the SCM systems and the simplification of the SCM challenges and other factors affecting its success.

A questionnaire is designed for measuring the elements of the proposed model. The questionnaires are revised by experts with experiences in SCM. For statistical analysis, SPSS 24.0 and SMART- PLS (partial least squares) 3.2.6 software package are used. The structural equation modeling (SEM) analysis procedure is conducted in two stages. The reliability analysis and confirmatory factor for analyzing the dimensions and items are included in the first stage. The second stage involves evaluating the assumptions through the SEM.

The results have depicted that four variables (knowledge sharing, VANET, RFID and NFC, and the social capabilities of using IT) affect the success of SCM systems.

This research specifies the factors impacting on the success of SCM in the organizations. These technologies aid companies in improving their performance in the SCM and facilitating coherence and collaboration.

Traffic density is one of the most important parameters to consider in the traffic operation field. Owing to limited data sources, traditional methods cannot extract traffic density directly. In the vehicular ad hoc network (VANET) environment, the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction technologies create better conditions for collecting the whole time-space and refined traffic data, which provides a new approach to solving this problem.

On that basis, a real-time traffic density extraction method has been proposed, including lane density, segment density and network density. Meanwhile, using SUMO and OMNet++ as traffic simulator and network simulator, respectively, the Veins framework as middleware and the two-way coupling VANET simulation platform was constructed.

Based on the simulation platform, a simulated intersection in Shanghai was developed to investigate the adaptability of the model.

Most research studies use separate simulation methods, importing trace data obtained by using from the simulation software to the communication simulation software. In this paper, the tight coupling simulation method is applied. Using real-time data and history data, the research focuses on the establishment and validation of the traffic density extraction model.

The purpose of this paper is to give an insight in to the routing protocols in Vehicular ad hoc Network (VANET). In this direction, for the efficient data dissemination in VANETs, a street-based forwarding protocol (SBFP) has been proposed.

The interferences among different street segments are considered and a unique street-based forwarding concept is introduced to reduce the local maximum problem. In this protocol, the greedy forwarding concept along with the broadcasting mechanism and suppression technique is implemented to minimize the overhead created in the regular beacons forwarding processes. QualNet simulator is used to implement and simulate SBFP. SUMO and MOVE tools are used to create the vehicle’s movement pattern and network topology.

The simulated results demonstrated improved packet delivery ratio (PDR) with a minimum average number of broadcast by each individual vehicle in the proposed SBFP than in its peer protocols.

This paper will be discussing a unique street-based forwarding technique exploring the advantages of global positioning system to obtain the location of vehicles and streets. This technique results in higher PDR and reduced network overhead.

Riding on the wave of intelligent transportation systems, the vehicular ad hoc network (VANET) is becoming a popular research topic. VANET is designed to build an environment where the vehicles can exchange information about the traffic conditions or vehicle situation to help the vehicles avoid traffic accidents or traffic jams. In order to keep the privacy of vehicles, the vehicles must be anonymous and the routing must be untraceable while still being able to be verified as legal entities. The paper aims to discuss these issues.

The exchanged messages must be authenticated to be genuine and verified that they were sent by a legal vehicle. The vehicles also can mutually trust and communicate confidentially. In VANETs, road-side units (RSUs) are installed to help the vehicles to obtain message authentication or communicate confidentially. However, the coverage of RSUs is limited due to the high cost of wide area installation. Therefore the vehicles must be able to obtain message authentication by themselves – without an RSU.

The authors take the concept of random key pre-distribution used in wireless sensor networks, modify it into a random secret pre-distribution, and integrate it with identity-based cryptography to make anonymous message authentication and private communication easier and safer. The authors construct a two-tier structure. The tier 1, trust authority, assigns n anonymous identities and embeds n secrets into these identities to be the private secret keys for the tier 2, registered vehicles. At any time, the vehicles can randomly choose one of n anonymous identities to obtain message authentication or communicate confidentially with other vehicles.

The processes of building neighbor set, setting pairing value, and message authenticating are proposed in this paper. The proposed method can protect against the attacks of compromising, masquerading, forging, and replying, and can also achieve the security requirements of VANET in message authentication, confidential communication, anonymity, and un-traceability. The performance of the proposed method is superior to the related works.

Software-define vehicular networks (SDVN) assure the direct programmability for controlling the vehicles with improved accuracy and flexibility. In this research, the resource allocation strategy is focused on which the seek-and-destroy algorithm is implemented in the controller in such a way that an effective allocation of the resources is done based on the multi-objective function.

The purpose of this study is focuses on the resource allocation algorithm for the SDVN with the security analysis to analyse the effect of the attacks in the network. The genuine nodes in the network are granted access to the communication in the network, for which the factors such as trust, throughput, delay and packet delivery ratio are used and the algorithm used is Seek-and-Destroy optimization. Moreover, the optimal resource allocation is done using the same optimization in such a way that the network lifetime is extended.

The security analysis is undergoing in the research using the simulation of the attackers such as selective forwarding attacks, replay attacks, Sybil attacks and wormhole attacks that reveal that the replay attacks and the Sybil attacks are dangerous attacks and in future, there is a requirement for the security model, which ensures the protection against these attacks such that the network lifetime is extended for a prolonged communication. The achievement of the proposed method in the absence of the attacks is 84.8513% for the remaining nodal energy, 95.0535% for packet delivery ratio (PDR), 279.258 ms for transmission delay and 28.9572 kbps for throughput.

The seek-and-destroy algorithm is one of the swarm intelligence-based optimization designed based on the characteristics of the scroungers and defenders, which is completely novel in the area of optimizations. The diversification and intensification of the algorithm are perfectly balanced, leading to good convergence rates.

This paper is a review of a number routing protocols in the internet of vehicles (IoV). IoV emphasizes information interaction among humans, vehicles and a roadside unit (RSU), within which routing is one of the most important steps in IoV network.

In this paper, the authors have summarized different research data on routing protocols in the IoV. Several routing protocols for IoV have been proposed in the literature. Their classification is made according to some criteria such as topology-based, position-based, transmission strategy and network structure. This paper focuses on the transmission strategy criteria. There exist three types of protocols that are based on this strategy: unicast protocol, broadcast protocols and multicast protocols. This later type is classified into two subclasses: geocast and cluster-based protocols. The taxonomy of the transmission strategy is presented in this study. We discuss the advantages and disadvantages of each type with a general comparison between the five types.

The authors can deduce that many challenges are encountered when designing routing protocols for IoV.

A simple and well-explained presentation of the functioning of the IoV is provided with a comparison among each categories of protocols is well presented along with the advantages and disadvantages of each type. The authors examined the main problems encountered during the design of IoV routing protocol, such as the quick change of topology, the frequent disconnection, the big volume of data to be processed and stored in the IoV, and the problem of network fragmentation. This work explores, compares existing routing protocols in IoV and provides a critical analysis. For that, the authors extract the challenges and propose future perspectives for each categories of protocols.

The purpose of this paper is to introduce a new data dissemination model in order to improve the performance of transmission in VANET. It proposes a protocol named Epidemic and Transmission-Segment-based Geographic Routing (ETSGR) and outlining the issues due to high mobility of nodes and uncertain physical topologies in the network. The proposed ETSGR is mainly used to analyze the vehicle state, direction, distance, traffic density and link quality of the network.

This research work based on ETSGR protocol mainly uses epidemic algorithm in order to find the vehicle state based on susceptible, infected and recovered (SIR) model. Furthermore, the vehicle position and finding the head node in the network is utilized using the transmission segment protocol based on geographic routing and analyses each node to form the segments and find the destination to transmit the data in timely manner.

The paper provides the enhancement of the performance based on some metrics such as end-to-end delay that obtained 0.62%, data throughput as 32.3%, packet delivery ratio as 67% and one-hop communication as 13%. The proposed ETSGR protocol analyzes the state of the vehicle correctly and each node segmented to transmit the data with the timely manner and obtaining reliable performance even with high mobility of nodes in the network.

The proposed ETSGR protocol may have some limitation when considering the timing which should improve even in increasing many number of vehicles and different road segments.

This paper includes some suggestions for the practical deployment of the approach in which a real-time traffic analysis can be evaluated for taking prior actions during an emergency situation and proper dissemination of data in timely manner can help utilize the guidance of proper planning of roads.

This research fulfills an enhanced protocol to improve the performance of data dissemination.