Towards efficient data collection mechanisms in the

Received: 20 May 2018

Revised: 20 November 2018

Accepted: 22 November 2018

DOI: 10.1002/dac.3893

RESEARCH ARTICLE

Towards efficient data collection mechanisms in the vehicular ad hoc networks Behrouz Pourghebleh1

| Nima Jafari Navimipour2

1

Young Researchers and Elite Club, Urmia Branch, Islamic Azad University, Urmia, Iran 2

Young Researchers and Elite Club, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran Correspondence Nima Jafari Navimipour, Young Researchers and Elite Club, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran. Email: [email protected]

Summary Recently, the fast growth of communication technology has led to the design and implementation of different types of networks in different environments. One of these remarkable networks is vehicular ad hoc network (VANET). The ubiquitous connectivity among vehicles is possible through VANET in the absence of fixed infrastructure. Moreover, it provides safety and comfort to people sitting in the vehicles. In this regard, collecting information from vehicles that are moved constantly is an essential challenge. To develop an efficient method for data collection in the VANET, some parameters must be considered such as data aggregation, latency, packet delivery ratio, packet loss, scalability, security, transmission overhead, and vehicle density. Since data collection has a significant importance in the VANET, the aim of this study is to investigate the existing methods and describe the types of important issues and challenging problems that can be addressed in data collection in the VANET. The data collection techniques are investigated in four primary groups, namely, topology‐based, cluster‐based, geocast‐based, and fog‐based. Also, the mentioned parameters are important to compare all of the presented techniques. KEYWORDS communication architecture, data collection, VANET, vehicle‐to‐vehicle communication, vehicular ad hoc networks

1 | INTRODUCTION One of the growing disruptive innovation is the Internet of things (IoT), which allows physical devices to communicate over heterogeneous networks.1-3 Moreover, one of the IoT applications is vehicular ad hoc network (VANET),4 which has been developed as a powerful technology aimed at providing safety and comfort to people sitting in the vehicles.5,6 Moreover, it affords wireless communication among vehicles, and dynamic traffic data is transferred with a high accuracy and low cost.7,8 The VANET is a module of intelligent transportation systems (ITS) and a subdivision of mobile ad hoc network (MANET).9,10 Typically, the vehicles are armed with sensing equipment and have a high storage and processing capacity.11 The unique features of the VANETs include higher node density, instability of wireless channel and limited bandwidth, predictability of vehicular pathway, highly dynamic network topology that change quickly and repeatedly, sufficient energy and resources, and hard delay constraints.12-14 A large scale of VANET is foreseen to be available in the near future because of the rapid growth of vehicles equipped with communication abilities.15 The data dissemination and the data collection of vehicles are two significant factors supported by VANET.16 Int J Commun Syst. 2019;e3893. https://doi.org/10.1002/dac.3893

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In the VANET, the vehicles are moving constantly and generate a noteworthy amount of events and data, thus, the effective collection of information is a fundamental challenge.17 Data collection in the VANET is considered as a remarkable application aimed at providing road traffic data, environmental information, and other types of data.18 Mostly, the road traffic information is collected by fixed traffic sensors.19 Firstly, the vehicles send data packets to the base station on the roadside, then, the collected data is sent to the data center through the base station.20 Overhead, information saturation time, scalability, reliability, and latency are considered as the performance parameters for collecting a critical information.21 The data collection scenario is faced with multiple challenges. First, most VANET applications are designed with real‐time requirements. Therefore, the timeliness of data is very important.22 Moreover, the communications in the VANET are affected by traffic conditions. Accordingly, the data collection methods should be consistent with traffic conditions.23 Furthermore, the amount of data to be transmitted by the vehicle could be huge, and the data collection method should consider the network communication overhead.20 The aim of this study is to evaluate the data collection techniques by answering the following research questions. 1. RQ1: What is the importance of data collection in the VANET? What are the main challenges of routing protocols in the VANET? 2. RQ2: How is the article searching and selecting to evaluate the data collection methods in the VANET? 3. RQ3: What are the significant parameters of the data collection in the VANET? 4. RQ4: What are the open issues in the field of data collection in the VANET? 5. RQ5: What are the limitations of the current study? The data collection has a significant importance in the VANET. This study aims to investigate the existing methods and outline the categories of important issues that can be addressed in data collection in the VANET. This paper is the first study that examines the data collection techniques in the VANET systematically. Therefore, the contributions of this paper are as follows: 1. 2. 3. 4.

Presenting an organized study of the existing techniques for data collection in the VANET. Providing a summary of the challenges in the field of data collection in the VANET. Offering classification and assessment of the investigated techniques and highlighting their features. Outlining crucial areas for improving data collection techniques in future researches.

The rest of this paper is classified as follows. Section 2 surveys the related work. The backgrounds of VANET and data collection are presented in Section 3. Section 4 explains the article selection process. Section 5 explains the selected data collection techniques in four classes. Section 6 offers the results and comparison of selected mechanisms. The open issues and the conclusion are discussed in Sections 7 and 8, respectively. The limitations of the study are discussed in Section 8.

2 | RELATED WORK Di Francesco et al24 have proposed a comprehensive study of data gathering methods in the wireless sensor network (WSN) based on mobile elements. They have examined the methods in three categories, including data transfer, discovery, and routing. However, the study is not systematic, and it does not investigate the energy‐saving methods of data collection techniques. Moreover, the future works have not been checked. The data collection techniques in the WSN have been surveyed by Dubey and Agrawal.25 Eliminating interference is in the center of attention of many data collection techniques. Some new wireless ad hoc networks are required to improve the energy consumption, scalability, quality of service (QoS), cost, fast data transfer, and fault tolerance. They have analyzed the methods based on some parameters such as spatial relationship, temporal relationship, overhead, scalability, and their weaknesses. However, the review is not a systematic study, and it does not discuss future issues and challenges. In another survey, Mukherjee et al26 have discussed the data gathering methods using mobile nodes in the WSN. They have explained the various types of mobile nodes used in data collection. The reviewed methods in the study

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are categorized into a mobile sink or a mobile relay node. Some parameters such as the number of mobile collectors, their mobility patterns, and speed are used to compare the reviewed data collection methods. Moreover, this method provides suggestions for future work. However, only a few lists of the methods have been examined, and the survey is not a systematic study. A review of data collection and routing methods in WSN has been proposed by Ali.27 He has classified the methods into three categories, including centric type, nature‐wise, and sensor network, and described some advantages of each method. Moreover, the comparison of the methods is described based on some factors such as the number of nodes, type of clustering, compressive sensor, and next cluster head selection process. However, recently published articles have not been reviewed, and challenging problems have not been provided. Malik and Pandey28 have proposed a comprehensive study of data collection methods in the VANET aimed at finding the best method. Static and dynamic are two classifications of related methods. The dynamic approaches are more efficient than static approaches in terms of packet delivery ratio, latency, and overhead. Furthermore, different techniques have been evaluated in two categories such as Roadside Unit (RSU), initiated (RI), and Vehicle‐Initiated Broadcast (VIB) mode, which has four modes, namely, VIB‐new segment, VIB‐complete path, vehicle initiated‐RSU find mode (VIR), and VIR‐new segment. The techniques have been assessed based on the mentioned factors. However, the survey does not compare the reviewed methods, and the advantages and weaknesses of each technique are not specified. A review of economic analysis and pricing methods, as well as their applications for data collection in the IoT, has been offered by Luong et al.29 Some issues such as resource and power allocation, topology formation and data exchange, security, and sensing coverage are addressed through these methods. Game theory and auction‐based pricing, economic concepts‐based pricing, and optimization‐based pricing are three classifications of these methods. They also have studied the current approaches in each group. However, the process of article selection for the study is not clear. Furthermore, Gupta and Gupta30 have surveyed a mobile sink based on data collection techniques in the WSN. Some performance metrics such as delay, tour length, energy consumption, throughput, and network lifetime are considered to examine the techniques. The comparison results have shown that most of the methods have balanced energy consumption and low delay. The main advantages of each method are described, but the weaknesses of the methods are not studied. Furthermore, the future works are not discussed. Moreover, Infanteena and Anita31 have proposed a survey of data gathering techniques in the WSN. They have examined the techniques in two categories, including compression‐based and network‐based. They have analyzed the techniques based on some parameters such as transmission overhead, energy consumption, energy efficiency, routing, data fidelity, communication cost, latency, data recovery error, and communication overhead. Although the advantages of each method have been analyzed, the weaknesses of methods have not been investigated. Moreover, this study does not offer any suggestions for future work. Finally, a review of video‐based vehicular traffic data gathering techniques has been proposed by Kavitha and Chandrappa.32 They have specified the type of information, accuracy, main features, advantages, weaknesses, and the technologies involved in each method. However, the review is not a systematic study, and they have not offered any suggestions for future work. There is no systematic study of the current data collection techniques in the VANET to prove the essential role of data collection in the VANET. With regards to the abovementioned gaps, the weaknesses of most of the reviewed articles are as follows: 1. 2. 3. 4.

The articles have not offered any suggestion for further studies and future work. Most of the articles are about the data collection in the WSN. The process of article selection for review is not clear. Some of the recently offered methods have not been studied.

3 | B ACKGR OU N D S In this section, the related terminologies and basic concepts about VANET and data collection in the VANET are presented.

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3.1 | VANET architecture Dedicated short‐range communication (DSRC) based on IEEE 802.11p is a key enabling wireless technology that provides architecture for vehicles within a vehicular environment to cover vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications.33 This method of communication offers safety applications and a wide range of information to drivers and travelers to have safe and comfortable driving.34 Vehicle to everything (V2X) communication covers the communications between V2V, V2I, vehicle to network (V2N), and vehicle to pedestrian (V2P). This promising wireless technology enables vehicles to share diverse information with other vehicles in the vicinity and remote infrastructures in a practical complex traffic condition.35,36 Generally, the V2X is a hybrid method to switch between V2V and V2I communication. The collaboration of these communications can guarantee a good connectivity, especially in situations where V2V communication is not always available, the V2I represents a solution to avoid the dropping connections.37 The IEEE 802.11p‐based V2X communication system faces many challenging problems such as limited coverage range, limited mobility support, high latency, lack of advanced use cases support, and low reliability, so special consideration should therefore be taken if used for time‐critical applications, such as active road safety.38 Cellular‐V2X (C‐V2X) communication is one of the novel paradigms introduced by the 3rd Generation Partnership Project (3GPP) in Release 14 that covers all of the aforementioned deficiencies.39 This technology provides a number of benefits, including deterministic security, low latency, more robust scalability, QoS guarantees, and a much larger coverage area.40 The cellular network here can be taken as referring to both current Long Term Evolution (LTE) technology and potential future 5G developments, as well as older standards. The Release 14 can only provide basic V2X applications like emergency vehicle warning, hazard warning, collision avoidance, and so on.41 The 3GPP launches the standardization activity for the first phase 5G system in Release 15 named New Radio (NR), which is divided into two phases, including Release 15 nonstandalone (NSA) as a priority and Release 15 full with standalone. Standalone operation refers to providing full data plane and controlling the plane functions in NR, while nonstandalone operation implies that the control plane functions of LTE are utilized as an anchor for NR.42 The 5G Automotive Association (5GAA) accelerates the development and deployment of C‐V2X to prepare the next generation vehicular communications towards connected and autonomous driving.43 The 5GAA can address all known use cases offering full flexibility for different business models.44 Sidelink is a feature of LTE first introduced in 3GPP Release 12 aiming at enabling V2V communications within legacy cellular‐based LTE radio access networks that includes two modes of operation. Both modes of Release 12 are designed aiming at improving the lifetime of mobile devices batteries, but these modes are not suitable for vehicular applications. Release 14 has introduced two new communication modes, including mode 3 and mode 4, which have been designed specifically for V2V communications. In mode 3, the radio resources used by vehicles are allocated by the cellular network. The mode 4 does not require cellular coverage, and the vehicles autonomously select the radio resources for their direct V2V communications using a distributed scheduling scheme supported by congestion control mechanisms. The Mode 4 is considered as the baseline mode and represents an alternative to 802.11p.45 RSUs, on‐board units (OBUs), and application units (AUs) are the main parts of VANET architecture.46 The RSU is an intelligent device deployed across the road network that delivers services to the vehicles.47 The OBU permits vehicles to make wireless communication among vehicular environment.48 The AU is a device equipped within the vehicle, which runs the applications that can use the OBUs' communication capabilities.49 The OBU and a set of sensors are required to equip each vehicle to gather and send the information to other vehicles or RSUs.50,51 In vehicular communication, the existing infrastructures, ad hoc fashion, or combining both skills are used to disseminate information. Pure cellular, pure ad hoc, and hybrid are a different classification of the vehicular network.52 The pure cellular architecture is shown in Figure 1. In this architecture, direct communication between vehicles is not possible, and making communication requires the being of RSU.53 This kind of VANET architecture is the so‐called V2I communication.54 In pure ad hoc architecture, the RSUs are not utilized to facilitate the vehicle communications. This kind of architecture is also called V2V communication, in which the sensors help vehicles to communicate directly.55 In this architecture, the economic limitations in the construction of cell towers and wireless access points lead to nodes being forced to work together.56 The pure ad hoc architecture is shown in Figure 2. The cellular and ad hoc architectures are combined to have a hybrid architecture of VANET.57 This kind of architecture is shown in Figure 3.

3.2 | Data collection in the VANET Delay tolerant and real‐time information are collected by data collection methods in the VANETs. In this regard, many safety and nonsafety associated applications such as commercial ads, road density, vehicles positions and average speed,

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FIGURE 1

Pure cellular architecture52

FIGURE 2

Pure ad hoc architecture52

FIGURE 3

Hybrid architecture52

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road incidents, congestion information, and upcoming or past events are supported.58 Furthermore, routing in the VANET is very challenging because of the high mobility of vehicles, dynamically changing the topology of the network, and frequent loss of communication.59 These features affect network performance in some cases such as latency and packet delivery ratio.60 The vehicles are likely to face a lot of impediments such as trees, buildings, traffic lights, and road junctions, which lead to the weakening of the quality of the channel and the communication.61 Therefore, the developed protocols for data collection in the VANET should be able to provide low latency, high throughput performance, and reliability.62 Providing safety and facilitating the users sitting in vehicles are the ultimate goals of routing in the VANET.63 Typically, the following levels are included in the data collection process in the VANET11:

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• Collection process initiation: The initiator of the collection process, which can be based on vehicle or RSU, must specify all input parameters for the data collection method such as the data type and collection area.64 • Data collection and aggregation: In this level, all informed vehicles must collect the designated data. The data collected by each vehicle before being sent to the initiator node can be aggregated with data collected by other vehicles. This process increases the performance of data collection operations.65 • Data delivery: After the data collection level, the collector vehicles must deliver the specified collected data to the initiator node.64

3.3 | Metrics In this paper, some performance measures such as data aggregation, latency, packet delivery ratio (PDR), packet loss, scalability, security, transmission overhead, and vehicle density are used to compare the existing data collection methods in the VANET. Therefore, a brief discussion of these metrics is offered in this section. • Data aggregation: The correlated information from different vehicles can be aggregated before it is delivered to the destination or other vehicles. Therefore, the communication overhead and costs can be decreased significantly by this process.66 • Latency: The time interval required for sending a packet from the source to the RSU is called latency. The amount of latency is affected by the movement of the vehicles given that the speed of the vehicles causes the breakdowns of links.67 • PDR: It is defined as the ratio of data packets received to the total original packet generated by vehicles.68 • Packet loss: The packet loss happens when the packet does not reach its destination and denotes the ratio of lost packets overall the transmitted packets.69,70 • Scalability: The VANETs could be a large‐scale network. The data collection mechanisms should consider the operation of many unicast routing requests simultaneously. Furthermore, the conflict of routing requests among vehicles particularly in the intersection should be considered.71 • Security: Since the VANET is a network with high node density, instability of wireless channel and their communication is vulnerable, and attackers may exploit the VANET to send counterfeit data to trick other vehicles. Therefore, the vehicles must be assured that each communication has been begun from a dependable source node, and the messages are not varied by malicious nodes.72 • Transmission overhead: Additional data are combined with the message through the network towards a destination, and it uses a certain amount of available bandwidth. Increasing protocol is because of this extra data that is not contributed to the content of the message.68,73 • Vehicle density: The vehicle density of the VANET is one of the important parameters for evaluating the data collection methods. It is measured as the number of vehicles per unit distance. The communication among vehicles in high‐density conditions causes an abundance of data and high overhead. Moreover, it causes a load imbalance.74

4 | ARTICLE S ELE CT I ON PROC E SS The techniques of data collection in the VANET are developed using the systematic literature review (SLR). The SLR is an accurate evaluation and analysis of all researches that are related to a specific problem.75-79 The article selection process for choosing the articles for the SLR method includes three stages: Stage 1: An automatic search based on keywords In the first stage, to construct a search query, some of the keywords are used, including collection VANET, collection VANETs, collection vehicular network, collection vehicular networks, collecting VANET, collecting VANETs, collecting vehicular network, collecting vehicular networks, collect VANET, collect VANETs, collect vehicular network, collect vehicular networks, gathering VANET, gathering VANETs, gathering vehicular network, gathering vehicular networks,

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gather VANET, gather VANETs, gather vehicular network, and gather vehicular networks. Next, Google Scholar* as the main search engine is used to find the relevant articles for mentioned keywords. The result of the automatic search from conference and journal articles, notes, chapters, and books is 95 articles. The search is based on the article title and the year of publication between 2008 and 2017. Figure 4 shows the distribution of these articles with the year of publication. Stage 2: Selection based on the title of the articles and quality of the publisher The specific screening criteria are placed in this stage to ensure that only the qualified publications are included for review. Therefore, the survey studies, editorial notes, working articles, and articles that are written in non‐English are excluded. Finally, 54 articles are intended for examination. Stage 3: Publication and relevant analysis In the previous stage, 54 articles have been obtained. In stage 3, the obtained articles are reviewed by the authors, and the articles whose technique is directly related to the VANET are chosen. Therefore, 17 articles are accepted, and the rest are excluded. Generally, the articles published in the VANET domain are selected, so the method explains and improves some of the desired parameters explicitly and clearly.

5 | REVIEW OF SELECTED MECHANISMS In order to carefully examine the data collection methods, the articles can be categorized into different categories. Topology‐based, cluster‐based, geocast‐based, and fog‐based are four classifications of obtained articles in the previous section. Among four categories of the data collection mechanisms, seven out of 17 articles are categorized as topology‐ based (Table 1), five out of 17 articles are categorized as cluster‐based (Table 2), three out of 17 articles are categorized as geocast‐based (Table 3), and two out of 17 articles are categorized as fog‐based (Table 4). The review of selected mechanisms will be discussed in this section. Topology‐based, cluster‐based, geocast‐based, fog‐based data collection mechanisms for VANET are a different part of this section. In each category, the applied techniques and basic properties are investigated. In addition, differences, advantages, and weaknesses of each mechanism will be described.

5.1 | Topology‐based mechanisms This section describes the topology‐based mechanism. Then, the selected topology‐based mechanisms are reviewed. Eventually, in Section 5.1.3, the discussed mechanisms are analyzed and compared.

5.1.1 | Overview of the topology‐based mechanism The information about the communication links and network topology is used for routing in the topology‐based mechanisms.93 These protocols are categorized into proactive, reactive, and hybrid methods.94 The proactive methods keep the topology information about all the nodes, regardless of whether they are currently involved in communications or not.95 Furthermore, the topology information is exchanged between the nodes periodically.96 On the other hand, routing information in the reactive protocols is determined on demand, only when required for current communications. The routing information is continually updated, so this kind of reactive routing can be applied in the VANET. In order to make routing more efficient and scalable, the reactive and proactive routing protocols can be combined.56

5.1.2 | Overview of the selected topology‐based mechanisms Pacheco‐Paramo et al84 have offered an adaptive technique of two routing protocols such as low‐power and lossy (RPL) networks and connectivity‐aware routing (CAR) to data collection in the VANET. The RPL can create a tree from the *www.Scholar.google.com.

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FIGURE 4

TABLE 1

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Distribution of articles based on publication years

Distribution of topology‐based mechanisms by journal or conference names

Publisher

Year

Institute of Electrical and Electronics Engineers (IEEE)

Elsevier

TABLE 2

Journal/Conferences 80

2017 2016

Malik and Pandey Drira et al81

2016 2016 2015

Jiao et al82 Qin et al83 Pacheco‐Paramo et al84

Cloud Computing, Data Science & Engineering Transactions on Intelligent Transportation Systems Global Communications Global Communications Vehicular Networking

2017 2016

He and Zhang20 Turcanu et al16

Ad Hoc Networks Ad Hoc Networks

Distribution of cluster‐based mechanisms by journal or conference names

Publisher

Year

Elsevier

Author

Vehicular Communications

Brik et al

2016

Institute of Electrical and Electronics Engineers (IEEE)

Journal/Conferences 11

2016

Wiley

TABLE 3

Author

Brik et al

64

Wireless Communications and Mobile Computing 85

2015 2014 2013

d'Orey et al Bouali et al86 Liu et al87

Vehicular Technology Global Communications Wireless Communications and Networking

Distribution of geocast‐based mechanisms by journal or conference names

Publisher

Year

Author

Journal/Conferences

Institute of Electrical and Electronics Engineers (IEEE)

2011 2011 2011

Lee et al88 Delot et al89 Zarmehri and Aguiar90

Advanced Communication Technology Mobile Data Management Vehicular Networking

TABLE 4

Distribution of fog‐based mechanisms by journal or conference names

Publisher

Year

Author

Journal/Conferences

Institute of Electrical and Electronics Engineers (IEEE)

2017 2017

Lai et al91 Lai et al92

Sensors Web and Big Data

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vehicles towards the RSU and establish a path from each vehicle through a single type of message. It controls the overhead through the use of a trickle timer. A hybrid routing method is called CAR, which founds full paths before data transmission. It updates them in order to adapt to dynamic conditions. The error correction procedures are performed through CAR. The mechanism has low latency, and it reduces the overhead and improves the PDR. However, in high‐ density condition, the PDR is dropped, and it causes a packet loss. Drira et al81 have presented a data collection mechanism using a 3G/LTE communication network to collect data efficiently from vehicles. It presents the data collection of gas emission and fuel consumption. The real taxi traces are used to assess the performance of the mechanism in terms of bandwidth savings. Moreover, measuring the impact of data aggregation on effectiveness has been evaluated in a simulation environment. Reducing the data storage requirement and communication load is among the capabilities of this mechanism. This mechanism is able to maintain a high accuracy for estimating fuel consumption and emission. Moreover, the amount of data transmitted in the network is reduced. However, the PDR and packet loss problems are not considered. A predictive data collection method has been suggested by Jiao et al.82 A road‐traffic data is gathered through this method. In this mechanism, the scheduling and routing decisions are made in a centralized manner in accordance with real‐time network status, eg, real‐time vehicle speed, position, and the road on which a vehicle runs. To realize this, a software‐defined network (SDN) is utilized. In the mechanism, the receiver candidate selection technique is designed to avoid packet loss. Improving transmit efficiency and PDR and high scalability are the benefits of this mechanism. But, high latency remains as a problem. Unified data collecting with vehicular networks has been suggested by Qin et al.83 First, the data collection efficiency is described as the function of the temporary data paths towards the candidate, ie, the sequences of pairwise contacts are ordered in time to forward data from vehicles to each of the candidates and formulate the problem as a multi‐objective optimization problem. Then, the regular relations among the candidates are revealed, and data paths are identified by combination graphs extracted from a large collection of GPS tracking. Reachability from the vehicles to each candidate and expected importance of the candidates are derived from the possible paths, which form the basis for a common algorithmic framework developed to decide the base station deployment and corresponding forwarding strategy afterward. The performance of this method is proved by trace‐driven simulations. The offered method improves the PDR and reduces latency and offers better scalability, but high computation overhead remains as a problem. Furthermore, Turcanu et al16 have proposed a mechanism that uses a multi‐hop network with only vehicle nodes to collect and disseminate information relating to the huge area. This mechanism is adaptive to different traffic conditions. The structure formed from vehicles to the data collector is used to collect data in a very quick and effective way. The suggested method is scalable and reduces the number of relay nodes; as a result, transmission overhead is reduced, but the packet loss problem is not considered. Malik and Pandey80 have proposed a secure and efficient method for data collection in the VANET based on an asymmetric encryption. It ensures security and confidentiality of the data exchange among vehicles and RSU. A secure authentication is established among the vehicles and RSU before the process of data collection. The suggested mechanism increases the PDR and reduces latency, but it has low scalability, and it does not consider the transmission overhead. Finally, the problem of data collection in the VANET has been studied by He and Zhang20 based on the rapid growth of traffic conditions. In this case, an adaptive method has been suggested to forward the data packets based on existing traffic information. Satisfying the data collection time constraint and decreasing the network communication overhead are the goals of this mechanism. They have formulated the data collection problem as a scheduling optimization problem and have proved that it is an NP‐complete problem. Furthermore, the problem is solved by developing an optimal dynamic programming solution and a genetic algorithm‐based heuristic solution based on the situation of different applications. The examination results have shown that the mechanism has effectiveness and efficiency, but it is very difficult to combine the data from different vehicles before sending to the base station. Moreover, high transmission overhead remains as a problem.

5.1.3 | Summary of the topology‐based mechanisms This section examines the selected topology‐based mechanisms. These mechanisms can increase the PDR and decrease latency, but some issues such as packet loss and aggregation of data should be solved in the future. The comparison, advantages, and weaknesses of each analyzed mechanism in this category are shown in Table 5.

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TABLE 5

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A comparison of the reviewed topology‐based mechanisms

Paper

Technique

Advantage

Weakness

Pacheco‐Paramo et al84

An adaptive method of the two routing protocols

•Low latency •Low transmission overhead

•Weak to work with high vehicle density

•High packet delivery ratio (PDR) •High scalability

•Without considering packet loss

Drira et al81

Development and testing of a 3G/LTE adaptive data collection scheme

•Considering data aggregation •Low transmission overhead •Low latency

•Without considering PDR •Without considering packet loss

Jiao et al82

Predictive big data collection

•High PDR •Low packet loss •High security •High scalability

•High latency

Qin et al83

On unified mobile sensing data gathering •Low latency •High PDR •High scalability •Low packet loss

Turcanu et al16

An integrated vehicular ad hoc network (VANET)–based data collection and dissemination method

Malik and Pandey80

Asymmetric encryption‐based secure and •Low latency efficient data collection method •High PDR •High security

•Low scalability •Without considering transmission overhead

He and Zhang20

Cost‐efficient traffic‐aware data collection •High PDR method •Low latency •High scalability

•High transmission overhead

•Low transmission overhead •Considering data aggregation •High PDR •Low latency •High scalability

•High transmission overhead

•Without considering packet loss

5.2 | Cluster‐based mechanisms In this section, the cluster‐based mechanism is described. Then, the selected cluster‐based articles are reviewed. Eventually, in Section 5.2.3, the discussed mechanisms are examined and compared.

5.2.1 | Overview of the cluster‐based mechanism In the cluster‐based mechanism, the adjacent vehicles are virtually grouped in a cluster. The cluster head, cluster member, or cluster gateway are the roles that each vehicle in each cluster may have. Every cluster has one cluster head that performs intracluster transmission and forwards data. A cluster gateway is a noncluster head vehicle responsible for intercluster links to establish a connection among the cluster and neighboring clusters. A cluster member is commonly called a regular vehicle with no intercluster link.97

5.2.2 | Overview of the selected cluster‐based mechanisms A compressive sensing‐based data collection (CS‐DC) as a reliable and scalable data collection method for the VANET has been offered by Liu et al.87 To have stable clusters, this mechanism suggests the distance and a mobility‐based clustering method. Local data will be reliably collected through this method, and it is very helpful to achieve data spatial relevance. Encoding or decoding the in‐network data in data collection is done through this mechanism. In addition, it ensures efficient communication and accurate data recovery. The simulation results have shown that the technique improves the efficiency, scalability, and reliability. Moreover, it reduces the overhead when the vehicle traffic density increases. However, high latency remains as a problem.

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Bouali et al86 have proposed a secure intersection‐based routing method for data collection in the VANET. An intercluster communication solution is suggested to evaluate the performance of vehicles and remove the malicious ones from the network where the cluster head is the only node responsible for measuring the trust level of different vehicles. However, the way of selecting cluster heads and adjusting threshold is not clear. Moreover, the mechanism has high overhead, and the cluster head centralization approach causes a high latency. d'Orey et al85 have presented an approach for data collection and dissemination for VANET. Transferring information from a central entity into a geographic area is the main aim of this mechanism, which is done by providing suitable uplink performance. In this regard, virtual infrastructure such as mobile infrastructure nodes is used. A centralized method is suggested for creating clusters and for selecting cluster heads. A remote server is used to perform the clustering process. The results have shown that the mechanism reduces the communication overheads and costs. However, the impact of vehicle density and link quality parameters such as PDR and packet loss is not considered. Furthermore, a distributed data gathering method for VANET has been offered by Brik et al11 in which a medium access method is applied to use the channel in a distributed manner. A cross‐layer method is designed to be utilized for both urban and highway environments. Delay tolerant and real‐time data collection are supported by this method, and it performs all levels of data collection method. The collection area has many virtual segments. Each segment has many vehicles. One vehicle as a collector is selected for each segment to collect data from road traffic or other vehicle collectors. It includes four separate steps, including collection process initiation, data collection, and data delivery. The redundant and unwanted information is removed by the aggregation method to have less delay and overhead. The method has better scalability, and the performance analysis has shown that the offered mechanism outperforms the other methods in terms of latency, aggregation ratio, overhead, and the average waiting time by increasing the density of vehicles, but it does not consider the PDR. Finally, Brik et al64 have suggested a clustered data collection scheme for VANET. Collection initiation, collection and aggregation, and information delivery to RSU are three stages of this method. The mechanism supports both real‐time and delay‐tolerant data gathering. Moreover, it manages the medium access control through a reliable dynamic time division multiple access (RD‐TDMA) method. Collection segment (CS) and silence segment (SS) are two types of roads in the suggested mechanism. The communication should be done in CSs to prevent accidents between adjacent sections while it is not allowed to have communication in SSs. Data is gathered by a vehicle selected as a cluster head (CH) in each CS. The mechanism offers better scalability, and it has a number of benefits such as improving the PDR and reducing overhead, but it has high latency.

5.2.3 | Summary of the cluster‐based mechanisms The previous section is related to the analysis of selected cluster‐based techniques. In addition, each mechanism is briefly described, and the advantages and weaknesses of each article are presented. In these mechanisms, although scalability and reducing overhead are more considered by researchers, they have not attempted to improve the PDR and avoid packet loss. The comparison, advantages, and weaknesses of each mechanism in this category are shown in Table 6.

5.3 | Geocast‐based mechanisms In this section, the geocast‐based mechanism is described. Then, the selected geocast‐based mechanisms are reviewed. Finally, in Section 5.3.3, the discussed mechanisms are compared and analyzed.

5.3.1 | Overview of the geocast‐based mechanism In the geocast‐based mechanisms, a particular geographic area is defined. The source node sends the packet to all other vehicles in a geographic region or zone of relevance (ZOR).98 The ZOR is defined as a geographic area in which the vehicles receive messages.56 The zone of forwarding (ZOF) is used to make communication when the destination node is in another ZOR. The data packets are forwarded to other ZORs by the vehicle inside ZOF.93

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TABLE 6

A comparison of the reviewed cluster‐based mechanisms

Paper

Technique

Advantage

Weakness

Liu et al87

Compressive sensing–based data collection

•Low transmission overhead •High security •Considering data aggregation •High scalability

•High latency

Bouali et al86

A secure intersection‐based routing protocol

•High security •High scalability

•High transmission overhead

d'Orey et al85

Neighbor‐aware virtual infrastructure for data •Low transmission dissemination and collection overhead •Low latency •High scalability

•High latency •Without considering packet delivery ratio (PDR) •Without considering packet loss •Without considering vehicle density

11

Distributed data gathering protocol

•Low packet loss •Considering data aggregation •Low transmission overhead •Low latency •High scalability

•Without considering PDR

Brik et al64

An extended cluster‐based protocol

•Considering data aggregation •Low transmission overhead •High PDR •High scalability

•High latency

Brik et al

5.3.2 | Overview of the selected geocast‐based mechanisms The density of vehicles and the network traffic load are considered for collecting information by geographical routing technique offered by Lee et al.88 In this regard, a strong and effective forwarding path for data delivery can be determined by each forwarding node. The method considers both road traffic and network traffic simultaneously, and it is able to monitor the real‐time traffic status of adjacent roads without deploying the static nodes. The real‐time traffic information such as distance, network traffic node, and node density is very important in choosing an optimal route by forwarding node at an intersection. The offered mechanism improves the PDR and transmission throughput, but it does not consider latency and scalability. Furthermore, a distributed hash table (DHT) based on geocast routing method has been proposed by Delot et al89 for collecting data in the VANET. It is possible to obtain a consistent response by the sender of the query in this mechanism. A request is disseminated in the network in a limited time by allowed vehicle. Then, the results are delivered to the issuing vehicle of the query. The method offers a solution for drivers to share and collect data in the VANET. Moreover, the mechanism guarantees that the maximum amount of results will be delivered in a restricted time, and it is scalable. But, for distant distances, the data packet may be lost before it reaches the requester vehicle. Furthermore, it suffers from high latency and low reliability. Finally, Zarmehri and Aguiar90 have proposed a back off based on a per‐hop forwarding mechanism based on geocast routing for data collection in the VANET. The environmental data are collected that needs to be delivered to the static destination node whose position is known to the vehicles. The distance of the sender and receiver from the destination node is helpful for calculating the back off timer. In the mechanism, the messages are not exchanged among the neighbors periodically, so the communication overhead is decreased. High PDR, low latency, and high scalability are the features of the suggested method, but it does not consider packet loss.

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5.3.3 | Summary of the geocast‐based mechanisms The selected geocast‐based mechanisms are studied in the previous section. Moreover, a summary, advantages, and weaknesses of each mechanism are presented. The researchers in the reviewed mechanisms pay more attention to improve the PDR and reduce overhead, but some issues such as packet loss and data aggregation are not considered in those mechanisms. The side‐by‐side comparison, advantages, and weaknesses of each mechanism in this category are shown in Table 7.

5.4 | Fog‐based mechanisms This section describes the fog‐based mechanism. Then, the selected fog‐based mechanisms are reviewed. Eventually, the discussed mechanisms are analyzed and compared.

5.4.1 | Overview of the fog‐based mechanism Fog‐based VANET is a new paradigm of VANETs with the advantages of both fog computing and vehicular cloud.99 Fog node, eg, RSU as a cloud server at the edge of the vehicular network, offers special services that require location awareness, network context information, and ultra‐low latency.100 These nodes can collect, process, organize, and store traffic data in real time. Moreover, the vehicular fog computing architecture can facilitate or provide a wide range of vehicle‐ based services during the gathering and processing of a large amount of data from vehicular networks.101

5.4.2 | Overview of the selected fog‐based mechanisms An efficient continuous event monitoring and data collection framework based on fog nodes in the VANET has been proposed by Lai et al.91 A two‐level threshold method is adopted to prevent unnecessary data transmission. First, the nodes sense the environment in a low‐cost sensing mode. Then, the sensed data are sent to the RSU, and the confidence of events is calculated. If the probability of an event is high and exceeds some threshold, the nodes will transfer to event‐ checking phase, and the rest of nodes will be selected to transfer to the deep sensing mode to generate more accurate data of the environment. The offered mechanism reduces data transmissions and at the same time detects and collects the events, but it does not consider packet loss. Lai et al92 have suggested a fog‐based data collection technique in the VANET. The mechanism consists of three layers, including a network layer, the fog layer, and the cloud layer. In the network layer, various information is collected, including encounter data with the neighboring nodes, the connection log, and other network metadata. The contact probability of nodes is calculated, and the fog nodes provide instructions to nodes, so the nodes could cooperate with the fog nodes to select, filter, and reduce the local data that needs to be uploaded. Moreover, the vehicles could determine the network status, calculate the node similarities and the data density, and switch the data collection strategies and communication channel, so that data will be uploaded as soon as there is a communication opportunity. TABLE 7

A comparison of the reviewed geocast‐based mechanisms

Paper

Technique

Advantage

Lee et al88

A hybrid traffic geographic routing with cooperative •High packet delivery ratio traffic data collection method (PDR) •Low transmission overhead

Weakness •Without considering latency •Without considering scalability

Delot et al89

Decentralized pull‐based information gathering

•High PDR •High scalability

•High latency •High packet loss

Zarmehri and Aguiar90

Data gathering for sensing applications

•Low transmission overhead •High PDR •Low latency •High scalability

•Without considering packet loss

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In the fog layer, processing capacity and location characteristics of the fog nodes are utilized to optimize the efficiency and effectiveness of the network. The fog nodes could communicate with the cloud, obtain the data collection requirements, and analyze the semantic information of the application. In the cloud layer, the interface is provided for the VANET application to store the collected data from the fog layer. The cloud provides APIs for each application, receives processing requests from the fog nodes, and manages the global metadata information of the network. Furthermore, the cloud could cooperate with the fog nodes to generate a comprehensive data collection framework. The suggested mechanism reduces the transmission cost of gathering the sensed data and suppresses the unnecessary message transmissions. However, some of the link quality parameters such as PDR and packet loss are not considered.

5.4.3 | Summary of the fog‐based mechanisms The previous section is related to the analysis of selected fog‐based mechanisms. The advantages, disadvantages, and summary of each mechanism are presented. These mechanisms can provide high data rate and scalability with minimum response time, delay, and overhead, but some issues such as packet loss and PDR should be considered in the future. The comparison, benefits, and weaknesses of each mechanism in this category are shown in Table 8.

6 | RESULTS The topology‐based mechanisms have been able to decrease latency and improve the PDR based on investigated results in the previous sections, but some issues such as packet loss and aggregation of data should be discussed in the future. For instance, Discover16 collects and disseminates information in the massive area, and it is compatible with different traffic conditions. It improves the PDR and decreases latency and overhead. Furthermore, unified data collecting83 formulates the problem of data collection as a multi‐objective optimization problem. It reduces latency and increases the PDR, and it also provides better scalability. In the cluster‐based mechanisms, scalability and reducing overhead are more considered by researchers. However, they have not tried to increase the PDR and avoid packet loss. A cross‐layer mechanism designed to be adapted for both urban and highway environments is called distributed data gathering protocol (DDGP).11 It supports both delays‐ tolerant and real‐time data collection and implements all the stages of a data collection method. It offers better scalability and decreases latency and overhead. Moreover, CS‐DC87 decreases the overhead and improves the efficiency, scalability, and reliability. The geocast‐based mechanisms try to limit the network congestion and overhead by defining a forwarding region and limiting the flooding inside it. The network partitioning and the presence of undesirable neighbors that may prevent the forwarding of messages are the disadvantages of these mechanisms. The researchers in the selected geocast‐ based mechanisms pay more attention to improve the PDR and reduce overhead, but some issues such as packet loss and data aggregation are not considered in those mechanisms. The environmental data is collected to be delivered to the static destination node by a scalable method called Back off‐based Per‐hop Forwarding (BPF).90 It improves the PDR and decreases overhead and latency. In the fog‐based mechanisms, real‐time data processing is done on the edge, so the results can be provided quickly. Furthermore, by applying fog computing, the vehicular applications can provide high data rate and scalability with minimum response time, overhead, and delay. Eventually, the traffic among vehicles and cloud servers is reduced; as a result, the bandwidth of the network is saved. However, they have not attempted to improve the PDR and avoid packet loss. TABLE 8 Paper

A comparison of the reviewed fog‐based mechanisms Advantage

Weakness

91

•Low transmission overhead •High scalability •Low latency

•Without considering packet loss

Lai et al92

•Low transmission overhead •Low latency •High scalability

•Without considering packet loss

Lai et al

Technique

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Some parameters such as data aggregation, latency, PDR, packet loss, scalability, security, transmission overhead, and vehicle density are used to compare and evaluate the selected articles, whether the effect of these parameters may be beneficial or harmful. A summary of the discussed data collection mechanisms as well as their main properties is provided in Table 9. As shown in Figure 5, some parameters such as latency, PDR, scalability, transmission overhead, and vehicle density are more considered by researchers in the selected articles. Moreover, these results have shown that the data aggregation, packet loss, and security are not considered in most articles.

7 | OPEN ISSUES The investigated results in the previous sections show that there are some important problems that have not been considered in data collection in the VANET. Therefore, this section discusses some future research directions and open research issues. Some parameters such as data aggregation, packet loss, and security have not been considered in some selected articles. Therefore, applying these parameters to the data collection mechanism can be one of the challenges for future research. The future research challenges are detection of the relevant, expected, and needed information to broadcast, using real‐world vehicular routes and log data of vehicles. These challenges show the efficiency of the data collection mechanism. Moreover, reducing the time of response in real‐time applications, developing a machine‐learning technique to predict the ratio and activities of malicious vehicles, and providing methods to safeguard the privacy and security of vehicles are recommended for future studies. Other interesting studies for future research include an extension of methods that can monitor large urban areas through cooperation between several agents, combining methods with onboard sensors and data fusion algorithms to increase the perception of a vehicle, and building new ITS applications. Moreover, comparing the behavior of the data collection methods on various conditions such as highway, urban, and semi‐urban environments and examining the impact of the vehicle density are remarkable matters. Furthermore, adding a method to merge data during data collection processes in order to reduce message loss and overhead and improving the overall performance of the method can be considered as a remarkable line for further studies. Using mobile agents for data collection creates security problems, including privacy protection and the possibility of denial‐of‐service attacks by malicious nodes, which can be addressed in future studies. TABLE 9

An overview of the discussed data collection mechanisms and their comparison based on main metrics Packet Data Delivery Packet Transmission Vehicle Aggregation Latency Ratio (PDR) Loss Scalability Security Overhead Density

Category

Author Name

Topology‐ based

Pacheco‐Paramo et al84 Drira et al81 Jiao et al82 Qin et al83 Turcanu et al16 Malik and Pandey80 He and Zhang20

✗

✓

✓

✗

✓

✗

✓

✓

✓ ✗ ✗ ✓ ✗ ✗

✓ ✗ ✓ ✓ ✓ ✓

✗ ✓ ✓ ✓ ✓ ✓

✗ ✓ ✓ ✗ ✗ ✗

✗ ✓ ✓ ✓ ✗ ✓

✗ ✓ ✗ ✗ ✓ ✗

✓ ✗ ✗ ✓ ✗ ✗

✗ ✓ ✓ ✓ ✗ ✓

Liu et al87 Bouali et al86 d'Orey et al85 Brik et al11 Brik et al64

✓ ✗ ✗ ✓ ✓

✗ ✗ ✓ ✓ ✗

✗ ✗ ✗ ✗ ✓

✗ ✗ ✗ ✓ ✗

✓ ✓ ✓ ✓ ✓

✓ ✓ ✗ ✗ ✗

✓ ✗ ✓ ✓ ✓

✓ ✓ ✗ ✓ ✓

Geocast‐based Lee et al88 Delot et al89 Zarmehri and Aguiar90

✗ ✗ ✗

✗ ✗ ✓

✓ ✓ ✓

✗ ✗ ✗

✗ ✓ ✓

✗ ✗ ✗

✓ ✗ ✓

✓ ✗ ✓

✗ ✗

✓ ✓

✗ ✗

✗ ✗

✓ ✓

✗ ✗

✓ ✓

✓ ✓

Cluster‐based

Fog‐based

Lai et al91 Lai et al92

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Considered parameters in the discussed mechanisms

Finally, improving the data collection process with the cooperation between participants is very noticeable. The participants use the VANETs to exchange knowledge and collaboratively perform the sensing tasks assigned by the urban sensing center. The sensing quality and efficiency in a dynamic environment are increased through this method.

8 | C O N C L U S I O N A N D L I M I T A T IO N The past and the state of the art mechanisms in the field of data collection in the VANET are discussed systematically in this study. First, an overview of the VANET and data collection in the VANET is presented. Then, the research methodology in the data collection methods is explained, and the selected methods in four categories, namely, topology‐ based, cluster‐based, geocast‐based, and fog‐based, are investigated. The methods of each classification are discussed in terms of advantages and disadvantages. Also, all presented methods are compared based on some important parameters such as data aggregation, latency, PDR, packet loss, scalability, security, transmission overhead, and vehicle density. Furthermore, all discussed methods are compared side by side, and the recommendation for further studies are discussed. Generally, the researchers should solve the problem of each category. According to the results, most of the methods try to improve the PDR and scalability and reduce the transmission overhead and latency, but data aggregation, packet loss, and security are not considered in most methods. The topology‐based methods, in comparison with methods of other categories, have good features and can decrease latency and improve the PDR. The cluster‐based methods pay more attention to scalability and can decrease the transmission overhead. These methods try to maintain the network performance at a suitable level, although the network may have many mobile nodes. The geocast‐based methods have been able to improve the PDR and limit the network congestion and overhead by defining a forwarding area. In the fog‐based mechanisms, by applying fog computing, the vehicular applications can provide high data rate and scalability with minimum response time, overhead, and delay. In this article, the comprehensive study of the data collection techniques in the VANET is presented, but there may be some limitations as well. The non‐English articles on the data collection in the VANET are omitted. The related studies may be unpublished or may not have been indexed with search terms of this study. The topology‐based, cluster‐ based, geocast‐based, and fog‐based are four classifications of these articles, but they might be classified into different categories. ORCID Behrouz Pourghebleh https://orcid.org/0000-0001-7209-6302 Nima Jafari Navimipour https://orcid.org/0000-0002-5514-5536 R EF E RE N C E S 1. Atzori L, Iera A, Morabito G. Understanding the Internet of things: definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Netw. 2017;56:122‐140.

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2. Pourghebleh B, Navimipour NJ. Data aggregation mechanisms in the Internet of things: a systematic review of the literature and recommendations for future research. J Netw Comput Appl. 2017;97:23‐34. 3. Hamzei M, Navimipour NJ. Towards efficient service composition techniques in the Internet of things. IEEE Internet Things J. 2018;5(5):3774‐3787. 4. Fangchun Y, Shangguang W, Jinglin L, Zhihan L, Qibo S. An overview of Internet of vehicles. China Commun. 2014;11(10):1‐15. 5. Soleymani SA, Abdullah AH, Hassan WH, et al. Trust management in vehicular ad hoc network: a systematic review. EURASIP J Wirel Commun Netw. 2015;2015(1):146. 6. Manvi SS, Tangade S. A survey on authentication schemes in VANETs for secured communication. Veh Commun. 2017;9:19‐30. 7. Płaczek B. Selective data collection in vehicular networks for traffic control applications. Transp Res C Emerg Technol. 2012;23:14‐28. 8. Darwish T, Bakar KA. Traffic density estimation in vehicular ad hoc networks: a review. Ad Hoc Networks. 2015;24:337‐351. 9. Kakkasageri M, Manvi S. Information management in vehicular ad hoc networks: a review. J Netw Comput Appl. 2014;39:334‐350. 10. Purkait R, Tripathi S. Network condition and application‐based data adaptive intelligent message routing in vehicular network. Intl J Commun Syst. 2018;31(4). 11. Brik B, Lagraa N, Lakas A, Cheddad A. DDGP: distributed data gathering protocol for vehicular networks. Veh Commun. 2016;4:15‐29. 12. Liu J, Wan J, Wang Q, Deng P, Zhou K, Qiao Y. A survey on position‐based routing for vehicular ad hoc networks. Telecommun Syst. 2016;62(1):15‐30. 13. Khan S, Alam M, Fränzle M, Müllner N, Chen YA. Traffic aware segment‐based routing protocol for VANETs in urban scenarios. Comput Electr Eng. 2018;68:447‐462. 14. Latif S, Mahfooz S, Jan B, Ahmad N, Cao Y, Asif M. A comparative study of scenario‐driven multi‐hop broadcast protocols for VANETs. Veh Commun. 2018;12:88‐109. 15. Chaqfeh M, Lakas A, Jawhar I. A survey on data dissemination in vehicular ad hoc networks. Veh Commun. 2014;1(4):214‐225. 16. Turcanu I, Salvo P, Baiocchi A, Cuomo F. An integrated VANET‐based data dissemination and collection protocol for complex urban scenarios. Ad Hoc Networks. 2016;52:28‐38. 17. Drira W, Puthal D, Filali F, editors. ADCS: An adaptive data collection scheme in vehicular networks using 3G/LTE. Connected Vehicles and Expo (ICCVE), 2014 International Conference on; 2014: IEEE. 18. Brik B, Lagraa N, Yagoubi MB, Lakas A, editors. An efficient and robust clustered data gathering protocol (CDGP) for vehicular networks. Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications; 2012: ACM. 19. Wongdeethai S, Siripongwutikorn P. Collecting road traffic information using vehicular ad hoc networks. EURASIP J Wirel Commun Netw. 2016;2016(1):9. 20. He Z, Zhang D. Cost‐efficient traffic‐aware data collection protocol in VANET. Ad Hoc Networks. 2017;55:28‐39. 21. Kakkasageri M, Manvi S, Pitt J. Cognitive agent based critical information gathering and dissemination in vehicular ad hoc networks. Wirel Pers Commun. 2013;69(4):1107‐1129. 22. Studer A, Bai F, Bellur B, Perrig A. Flexible, extensible, and efficient VANET authentication. J Commun Networks. 2009;11(6):574‐588. 23. Li F, Wang Y. Routing in vehicular ad hoc networks: a survey. IEEE Veh Technol Mag. 2007;2(2):12‐22. 24. Di Francesco M, Das SK, Anastasi G. Data collection in wireless sensor networks with mobile elements: a survey. ACM Trans Sens Netw (TOSN). 2011;8(1):7. 25. Dubey S, Agrawal C. A survey of data collection techniques in wireless sensor network. Int J Adv Eng Technol. 2013;6(4):1664. 26. Mukherjee R, Roy S, Das A, editors. Survey on data collection protocols in wireless sensor networks using mobile data collectors. Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on; 2015: IEEE. 27. Ali AW, editor Energy efficieny in routing protocol and data collection approaches for WSN: a survey. Computing, Communication & Automation (ICCCA), 2015 International Conference on; 2015: IEEE. 28. Malik A, Pandey B. Performance analysis of various data collection schemes used in VANET. Indian J Sci Technol. 2015;8(15). 29. Luong NC, Hoang DT, Wang P, Niyato D, Kim DI, Han Z. Data collection and wireless communication in Internet of things (IoT) using economic analysis and pricing models: a survey. IEEE Commun Surv Tutorials. 2016;18(4):2546‐2590. 30. Gupta N, Gupta V, editors. A review on sink mobility aware fast and efficient data gathering in wireless sensor networks. Advances in Computing, Communication, & Automation (ICACCA)(Spring), International Conference on; 2016: IEEE. 31. Infanteena SD, Anita EM, editors. Survey on compressive data collection techniques for wireless sensor networks. Information Communication and Embedded Systems (ICICES), 2017 International Conference on; 2017: IEEE. 32. Kavitha N, Chandrappa D, editors. Comparative review on video based vehicular traffic data collection for intelligent transport system. Recent Advances in Electronics and Communication Technology (ICRAECT), 2017 International Conference on; 2017: IEEE.

18 of 20

POURGHEBLEH AND JAFARI NAVIMIPOUR

33. Lu N, Cheng N, Zhang N, Shen X, Mark JW. Connected vehicles: solutions and challenges. IEEE Internet Things J. 2014;1(4):289‐299. 34. Al‐Sultan S, Al‐Doori MM, Al‐Bayatti AH, Zedan H. A comprehensive survey on vehicular ad hoc network. J Netw Comput Appl. 2014;37:380‐392. 35. Muhammad M, Safdar GA. Survey on existing authentication issues for cellular‐assisted V2X communication. Veh Commun. 2018;12:50‐65. 36. Yang Y, Wei Z, Zhang Y, Lu H, Choo K‐KR, Cai H. V2X security: a case study of anonymous authentication. Pervasive Mob Comput. 2017;41:259‐269. 37. Vegni AM, Little TD. Hybrid vehicular communications based on V2V‐V2I protocol switching. Int J Veh Inf Commun Syst. 2011;2(3‐ 4):213‐231. 38. Khan Z, Fan P. A multi‐hop moving zone (MMZ) clustering scheme based on cellular‐V2X. China Commun. 2018;15(7):55‐66. 39. Abanto‐Leon LF, Koppelaar A, Math CB, de Groot SH. System Level Simulation of Scheduling Schemes for C‐V2X Mode‐3. arXiv preprint arXiv:180704822; 2018. 40. MacHardy Z, Khan A, Obana K, Iwashina S. V2x access technologies: regulation, research, and remaining challenges. IEEE Commun Surv Tutorials. 2018;20(3):1858‐1877. 41. Xia N, Yang C‐S. Vehicular Communications: Standards and Challenges. International Conference on Internet of Vehicles. Springer; 2017. 42. Lien S‐Y, Shieh S‐L, Huang Y, Su B, Hsu Y‐L, Wei H‐Y. 5G new radio: waveform, frame structure, multiple access, and initial access. IEEE Commun Mag. 2017;55(6):64‐71. 43. Djilali YD, Bakhtil Y, Kouninef B, Senouci B, editors. Performances evaluation study of VANET communication technologies for smart and autonomous vehicles. 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN); 2018: IEEE. 44. Abbas F, Fan P, Khan ZA. Novel low‐latency V2V resource allocation scheme based on cellular V2X communications. IEEE Trans Intell Transp Syst. 2018;1‐13. 45. Molina‐Masegosa R, Gozalvez J. LTE‐V for sidelink 5G V2X vehicular communications: a new 5G technology for short‐range vehicle‐to‐ everything communications. IEEE Veh Technol Mag. 2017;12(4):30‐39. 46. Leiding B, Memarmoshrefi P, Hogrefe D, editors. Self‐managed and blockchain‐based vehicular ad‐hoc networks. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct; 2016: ACM. 47. Xu Y, Wu Y, Wu G, Xu J, Liu B, Sun L, editors. Data collection for the detection of urban traffic congestion by VANETs. Services Computing Conference (APSCC), 2010 IEEE Asia‐Pacific; 2010: IEEE. 48. Shah SAA, Shiraz M, Nasir MK, Noor RBM. Unicast routing protocols for urban vehicular networks: review, taxonomy, and open research issues. J Zhejiang University SCIENCE C. 2014;15(7):489‐513. 49. Ghorai C, Banerjee I, editors. A multi‐objective data dissemination protocol for intelligent transportation systems. Advance Computing Conference (IACC), 2017 IEEE 7th International; 2017: IEEE. 50. RoselinMary S, Maheshwari M, Thamaraiselvan M, editors. Early detection of DOS attacks in VANET using Attacked Packet Detection Algorithm (APDA). Information Communication and Embedded Systems (ICICES), 2013 International Conference on; 2013: IEEE. 51. Hasrouny H, Samhat AE, Bassil C, Laouiti A. VANet security challenges and solutions: a survey. Veh Commun. 2017;7:7‐20. 52. Bilal SM, Bernardos CJ, Guerrero C. Position‐based routing in vehicular networks: a survey. J Netw Comput Appl. 2013;36(2):685‐697. 53. Jeremiah C, Nneka AJ, editors. Issues and possibilities in vehicular ad‐hoc networks (VANETs). Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE), 2015 International Conference on; 2015: IEEE. 54. Kumar S, Verma AK. Position based routing protocols in VANET: a survey. Wirel Pers Commun. 2015;83(4):2747‐2772. 55. Engoulou RG, Bellaïche M, Pierre S, Quintero A. VANET security surveys. Comput Commun. 2014;44:1‐13. 56. Sharef BT, Alsaqour RA, Ismail M. Vehicular communication ad hoc routing protocols: a survey. J Netw Comput Appl. 2014;40:363‐396. 57. Cunha F, Villas L, Boukerche A, et al. Data communication in VANETs: protocols. Appl Challenges Ad Hoc Netw. 2016;44:90‐103. 58. Allal S, Boudjit S, editors. Geocast routing protocols for vanets: survey and guidelines. Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2012 Sixth International Conference on; 2012: IEEE. 59. Das D, Misra R. Improvised dynamic network connectivity model for vehicular ad‐hoc networks (VANETs). J Netw Comput Appl. 2018;122:107‐114. 60. Bilal SM, Ali S. Review and performance analysis of position based routing in VANETs. Wirel Persl Commun. 2017;94(3):559‐578. 61. Abdel‐Halim IT, Fahmy HMA. Prediction‐based protocols for vehicular ad hoc networks: survey and taxonomy. Comput Networks. 2017. 62. Awang A, Husain K, Kamel N, Aissa S. Routing in vehicular ad‐hoc networks: a survey on single‐and cross‐layer design techniques. And Perspectives IEEE Access. 2017;5:9497‐9517. 63. Dua A, Kumar N, Bawa S. A systematic review on routing protocols for vehicular ad hoc networks. Veh Commun. 2014;1(1):33‐52.

POURGHEBLEH AND JAFARI NAVIMIPOUR

19 of 20

64. Brik B, Lagraa N, Lakas A, Cherroun H, Cheddad A. ECDGP: extended cluster‐based data gathering protocol for vehicular networks. Wirel Commun Mob Comput. 2016;16(10):1238‐1255. 65. Said AM, Marot M, Ibrahim AW, Afifi H. Modeling interactive real‐time applications in VANETs with performance evaluation. Comput Netw. 2016;104:66‐78. 66. Kumar R, Dave M. A framework for handling local broadcast storm using probabilistic data aggregation in VANET. Wirel Persl Commun. 2013;72(1):315‐341. 67. Mchergui A, Moulahi T, Alaya B, Nasri S. A survey and comparative study of QoS aware broadcasting techniques in VANET. Telecommun Syst. 2017;1‐29. 68. Gupta N, Prakash A, Tripathi R. Medium access control protocols for safety applications in vehicular ad‐hoc network: a classification and comprehensive survey. Veh Commun. 2015;2(4):223‐237. 69. Gowtham M, Subramaniam K. Congestion control and packet recovery for cross layer approach in MANET. Clust Comput. 2018;1‐8. 70. Neghabi AA, Navimipour NJ, Hosseinzadeh M, Rezaee A. Load balancing mechanisms in the software defined networks: a systematic and comprehensive review of the literature. IEEE Access. 2018;6:14159‐14178. 71. Lin Y‐W, Chen Y‐S, Lee S‐L. Routing protocols in vehicular ad hoc networks: a survey and future perspectives. J Inf Sci Eng. 2010;26(3):913‐932. 72. Daeinabi A, Rahbar AG. An advanced security scheme based on clustering and key distribution in vehicular ad‐hoc networks. Comput Electr Eng. 2014;40(2):517‐529. 73. Navimipour NJ, Milani FS. A comprehensive study of the resource discovery techniques in peer‐to‐peer networks. Peer‐To‐Peer Netw Appl. 2015;8(3):474‐492. 74. Wang S, Lei T, Zhang L, Hsu C‐H, Yang F. Offloading mobile data traffic for QoS‐aware service provision in vehicular cyber‐physical systems. Futur Gener Comput Syst. 2016;61:118‐127. 75. Navimipour NJ, Vakili A. Comprehensive and systematic review of the service composition mechanisms in the cloud environments. J Netw Comput Appl. 2017. 76. Hayyolalam V, Kazem AAP. A systematic literature review on QoS‐aware service composition and selection in cloud environment. J Netw Comput Appl. 2018;110:52‐74. 77. Chiregi M, Navimipour NJ. Cloud computing and trust evaluation: a systematic literature review of the state‐of‐the‐art mechanisms. J Electr Syst Inf Technol. 2017. 78. Asghari S, Navimipour NJ. Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments. Int J Commun Syst. 2018;31(12):e3708. 79. Alyari F, Jafari Navimipour N. Recommender systems: a systematic review of the state of the art literature and suggestions for future research. Kybernetes. 2018;47(5):985‐1017. 80. Malik A, Pandey B, editors. Asymmetric encryption based secure and efficient data gathering technique in VANET. Cloud Computing, Data Science & Engineering‐Confluence, 2017 7th International Conference on; 2017: IEEE. 81. Drira W, Ahn K, Rakha H, Filali F. Development and testing of a 3G/LTE adaptive data collection system in vehicular networks. IEEE Trans Intell Transp Syst. 2016;17(1):240‐249. 82. Jiao Z, Ding H, Dang M, Tian R, Zhang B, editors. Predictive big data collection in vehicular networks: a software defined networking based approach. Global Communications Conference (GLOBECOM), 2016 IEEE; 2016: IEEE. 83. Qin J, Zhu H, Zhu Y, Yu J, Xue G, Qian S, et al., editors. On unified mobile sensing data gathering with urban vehicular networks. Global Communications Conference (GLOBECOM), 2016 IEEE; 2016: IEEE. 84. Pacheco‐Paramo D, Iannone L, Kofman D, Viana AC, Mermoud G, Vasseur J‐P, editors. Adaptation of topology‐based routing protocols for data gathering applications in VANETs. Vehicular Networking Conference (VNC), 2015 IEEE; 2015: IEEE. 85. d'Orey PM, Maslekar N, de la Iglesia I, Zahariev NK, editors. NAVI: neighbor‐aware virtual infrastructure for information collection and dissemination in vehicular networks. Vehicular Technology Conference (VTC Spring), 2015 IEEE 81st; 2015: IEEE. 86. Bouali T, Aglzim E‐H, Senouci S‐M, editors. A secure intersection‐based routing protocol for data collection in urban vehicular networks. Global Communications Conference (GLOBECOM), 2014 IEEE; 2014: IEEE. 87. Liu C, Chigan C, Gao C, editors. Compressive sensing based data collection in VANETs. Wireless Communications and Networking Conference (WCNC), 2013 IEEE; 2013: IEEE 88. Lee J‐W, Lo C‐C, Tang S‐P, Horng M‐F, Kuo Y‐H, editors. A hybrid traffic geographic routing with cooperative traffic information collection scheme in VANET. Advanced Communication Technology (ICACT), 2011 13th International Conference on; 2011: IEEE. 89. Delot T, Mitton N, Ilarri S, Hien T, editors. Decentralized pull‐based information gathering in vehicular networks using GeoVanet. Mobile Data Management (MDM), 2011 12th IEEE International Conference on; 2011: IEEE. 90. Zarmehri MN, Aguiar A, editors. Data gathering for sensing applications in vehicular networks (poster). Vehicular Networking Conference (VNC), 2011 IEEE; 2011: IEEE.

20 of 20

POURGHEBLEH AND JAFARI NAVIMIPOUR

91. Lai Y, Yang F, Su J, et al. Fog‐based two‐phase event monitoring and data gathering in vehicular sensor networks. Sensors. 2017;18(1):82. 92. Lai Y, Zhang L, Wang T, Yang F, Xu Y (Eds). Data Gathering Framework Based on Fog Computing Paradigm in VANETs. Asia‐Pacific Web (APWeb) and Web‐Age Information Management (WAIM) Joint Conference on Web and Big Data. Springer; 2017. 93. Salman O, Morcel R, Al Zoubi O, Elhajj I, Kayssi A, Chehab A, editors. Analysis of topology based routing protocols for VANETs in different environments. Multidisciplinary Conference on Engineering Technology (IMCET), IEEE International; 2016: IEEE. 94. Al‐Turjman F. QoS‐aware data delivery framework for safety‐inspired multimedia in integrated vehicular‐IoT. Comput Commun. 2018;121:33‐43. 95. Pandey A, Deep V, Sharma P, editors. Enhancing ADOV routing protocol for vehicular ad hoc networks 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN); 2018: IEEE. 96. Sujatha S, Soundeswari P. Comparative and behavioral study on VANET routing protocols. IOSR J Comput Eng. 2012;5(2):41‐45. 97. Jalalvandi S, Rafeh R, editors. A cluster‐based routing algorithm for VANET. Computer and Communications (ICCC), 2016 2nd IEEE International Conference on; 2016: IEEE. 98. Ghafoor H, Aziz K, editors. Position‐based and geocast routing protocols in VANETs. Emerging Technologies (ICET), 2011 7th International Conference on; 2011: IEEE. 99. Wang L, Liu G, Sun L. A secure and privacy‐preserving navigation scheme using spatial crowdsourcing in fog‐based VANETs. Sensors. 2017;17(4):668. 100. Truong NB, Lee GM, Ghamri‐Doudane Y, editors. Software defined networking‐based vehicular adhoc network with fog computing. Integrated Network Management (IM), 2015 IFIP/IEEE International Symposium on; 2015: IEEE. 101. Huang C, Lu R, K‐KR C. Vehicular fog computing: architecture, use case, and security and forensic challenges. IEEE Commun Mag. 2017;55(11):105‐111.

How to cite this article: Pourghebleh B, Jafari Navimipour N. Towards efficient data collection mechanisms in the vehicular ad hoc networks. Int J Commun Syst. 2019;e3893. https://doi.org/10.1002/dac.3893