ITS applications on VANETs
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Although nowadays wireless networks are a regular and familiar framework for sharing information among devices, the way in which these nets are organized and managed is evolving day by day due to the requirements of the scenarios in which they are deployed. Since those first experiments carried out by the WECA (Wireless Ethernet Compatibility) association in late 90s, the application areas and use cases in where wireless communications are applied has been changing.
Many of the wireless networks that we use daily at home, at the office or when se use a cellular are based on those first approaches, in which an Access Point is needed to have connectivity. These setups are called ‘Infrastructure Mode’ and use a fixed and wired backbone to address information from the source AP to the destination AP. But in some situations these networks are limited by their own nature due to their need for an AP, a base station, some routers or switches and so on. It is in these scenarios where a ‘’Infrastructure-less Mode’’ can overcome these drawbacks, allowing the nodes of a network to routing and forwarding information for other nodes, without relying on centralized administrator. These types of networks are called wireless ad hoc networks [1].
Infrastructure based networks (left) and Ad hoc networks (right)
Now, if we have into consideration the current trends in technology, it can be said that mobility and ubiquity are common characteristics to all the new gadgets launched to the market. Users want to be online anytime and everywhere and to obtain information from all the surrounding elements. Then, we talk about Mobile Ad hoc Networks (MANETs), that is, wireless networks with a dynamic shape, a shifting number of nodes, a defined bandwidth and other characteristics, where the nodes can be any kind of devices with communications and networking capability that communicate with each other without a centralized coordinator [2]. In this scenario, each node can play the role of a router, hosting the network topology dynamically, because as it was mentioned above, the shape and the topology of the net can change as well as the nodes on it. The main characteristics of MANETs can be summarized as follows [3]:
Dynamic topologies: network topology can change quickly due to the nodes can move freely in the net.
Bandwidth constrains: compare with wired networks, the capacity of a MANET is relatively small and also it is sensitive to interferences, noise, and signal fading effect.
Energy constrains: although many of the nodes can be plugged to the power line or they can be equipped with big batteries, some of them use small power supplies, so during the network design it is necessary to consider how to save power in order to assure the stability and longevity of the network.
Limited physical security: although the decentralized nature of MANETs provides robustness against the single points of failure, these nets must be protected against eavesdropping, spoofing, and the injection of malicious data attacks.
In this context, thanks to the rapid increase and improvement of the mobile computing a wide set of wireless devices have proliferated, making possible that traditional hardware as digital cameras, thermostats, cooking ovens or washing machines are provided with communications and computing functionalities so they can be part of a MANET. This new paradigm is known as Internet of Things [4], that is, a scenario in which all the objects beyond computers, mobiles or touch screens have the ability of generating, sharing and processing information in a pervasive manner [5]. With all of this, technologies must have evolved to new standards, architectures, protocols, hardware, services and facilities that will make possible a control of the way in with all the nodes access to the net to share their information.
One scenario that represents perfectly the characteristics and it is a perfect case of study of MANETs is the Vehicular Ad hoc Networks (VANETs), a subset of MANETs, which creates wireless networks between vehicles [6]. In a VANET each vehicle is a moving node which creates wireless networks with surrounding vehicles [7], thanks to the On-Board Unit (OBU), a hardware with communications and computing capabilities that allows drivers to receive information about events that can affect his driving. Then, the main function of the OBU is to exchange information with other vehicles or Road Side Units (RSUs), elements located at the infrastructure that act as gateways between the VANET and other networks or agents as Traffic Management Centers (TMC). These centers are placed far away the VANET and play an important role in the applications developed in the area of VANETs, coordinating the information that is shared among VANETs that are deployed in different geographical areas.
In VANETs they can be distinguish two types of links: vehicular-to-vehicular communication (V2V), based on an Ad hoc architecture, vehicles exchange directly messages without a central coordinator; and vehicle-to-infrastructure or infrastructure-to-vehicle (V2I or I2V), where the messages are shared between the vehicles and the RSUs. VANETs are designed for a huge range of cooperative applications, that is, services that provide information to the drivers thanks to the data shared among all the vehicles on the net. These can be safety and non-safety applications, which allow several added services as infotainment, traffic management, toll payment, and geographical based services and so on [8]. That is, VANETs make possible to deploy applications that help to improving the transport services and traffic conditions using collaborative systems based on V2X Ad hoc networks.
Vehicular Ad hoc Network Scenario
This introduces the definition of Intelligent Transport Systems, where each vehicle is a sender, a receiver and a router at the same time, so it can broadcast the information to the VANET, which uses this information to provide these safety and non-safety services to the drivers. The OBU is the hardware in charge of processing these data and it also enables these short range wireless ad hoc networks (the coverage area is around 300 meters) but it also must dispose other systems that permit to report position information such as Global Positioning System (GPS) or a Differential Global Positioning System (DGPS) receiver if more accuracy position information is required. This information is quite important because most of the services that are available in a VANET depend on the geographical position of the source and the destination. Table 1 presents a classification of ITS applications that can be deployed using the VANET architecture [9].
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Active safety applications | \n\t\t\tCooperative driving assistance applications | \n\t\t\tEmergency vehicle warning Slow vehicle indication Intersection collision warning Motorcycle approaching indication Emergency electronic brake lights Wrong way driving warning Stationary vehicle - accident Stationary vehicle - vehicle problem Traffic condition warning Signal violation warning Roadwork warning Collision risk warning | \n\t\t
Efficiency applications | \n\t\t\tTraffic management Road monitoring | \n\t\t\tRegulatory / contextual speed limits notification Traffic light optimal speed advisory Enhance routing Road conditions sensing (rain, visibility, wind, hazardous location, road adhesion) | \n\t\t
Infotainment applications | \n\t\t\tContextual information Entertainment | \n\t\t\tPoint of Interest notification Automatic access control and parking management ITS local electronic commerce Media downloading Insurance and financial services Fleet management Loading zone management | \n\t\t
ITS applications on VANETs
These applications can be deployed on urban or motorway scenarios, each one with its own particularities. In an urban scenario, many of the times there is not line of sight between the nodes so fading and communication disruptions are frequents. In a motorway, the high density of vehicles can overload the radio channels in which the VANETs work. Yes, although maybe users are not aware about that, the radio spectrum (the physical interface used by wireless communications networks) is a limited resource that it must be shared among all OBUs and RSUs that shape the VANET. Commonly, ISM (Industrial Scientific Medical) radio bands with frequency ranges 2.40–2.4835 GHz and 5.15–5.875 GHz are used by wireless networks for license-free communications [10]. The definition of these standards is crucial in order to attend the increase on the demand of the spectrum channels and to make possible that different networks can coexist in the same radio band.
Although WLAN (IEEE 802.11a/b/g/n) could be the technology used in VANETs, most of the applications included at Table 1 require time-critical communications, a continuous handover among different RSU in V2I/I2V links, and as these standards use CSMA (Carrier Sense Multiple Access), so many of the nodes cannot have success in channel access due to the high density of some scenarios. Due to the limitations of these standards in mobile scenarios as VANETs, a new extension has been developed: IEEE 802.11p, designed specifically for vehicular environment in which high reliability and low delay characteristics are mandatory. This new standard, known as Dedicated Short Range Communication (DSRC) uses the physical layer of IEEE 802.11a working on the 5.9 GHz band and quality of service enhancements of IEEE 802.11e. Network and transport layers are in the scope of WAVE (Wireless Access in the Vehicular Environment) standard which defines the protocols and services that support multi-channel wireless connectivity between IEEE 802.11 Wireless Access in Vehicular Environments devices [11].
Once the access to the medium is defined under the frame of the IEEE 802.11p standard, in a situation in which many nodes have information to transmit to different destinations in a network that is geographically distributed, it is quite important to determine the protocols that allow to organize the addressing of the information and to assure that all the nodes have the chance of transmitting and receiving data. The nature of MANETs, and specifically of VANETs implies that the maintenance, management and routing task of the network must be done by all the nodes, making these kind of networks more difficult or more complex to other wireless networks. Therefore, advances techniques of management and arrangement should be applied to organize the network and assure its effective implementation and its fairness and reliability for all the nodes.
In the next sections of these chapter are analyzed the main techniques used to disseminate data in VANETs, with an special emphasis in clustering, a control scheme that can take into consideration the speed and distance difference among neighboring nodes in the VANET to group them in order to assure a stable cluster structure and then enhance the stability of the network topology.
Data dissemination in VANETs has recently received considerable attention. Due to the unique characteristics of VANET, the implementation of reliable data dissemination among vehicles has encountered many challenges. Information dissemination in VANETs provides drivers a way to be aware in real-time of everything that is happening in their surroundings. A wide range of information can be disseminated, including traffic and road conditions, closure and detour information, incident information, emergency alerts, and driver advisories.
Information dissemination schemes in VANETs are commonly categorized into two different groups, according to the type of ITS application: safety and non-safety. During the last years, research community has focused their studies more on safety applications which are highly demanding in terms of message delay and present a challenging field of study. Although in safety applications the frequency of messages is low, the message delay is a key factor because a safety message, e.g., an emergency vehicle warning, has to reach a maximum number of nodes in a given area within a very short time interval, because after this time interval, the message essentially becomes useless.
However, in non-safety applications the message delay loses importance since the message could be useful for a longer time interval, even up to several minutes, e.g., for disseminating traffic road conditions. On the other hand, the frequency of these messages is much higher in this type of applications.
Therefore, data dissemination in VANET is a challenge for the deployment of cooperative services and applications because the dissemination routing protocol has to be suitable both for safety and non-safety applications, and it also has to be aware of the vehicular environment challenges as the high mobility of nodes and the extremely dynamic network topology. Therefore, the design of an efficient information dissemination routing protocol for VANETs is very crucial.
The function of a routing protocol in Ad-Hoc network is to establish routes between different nodes and the main requirement is to achieve minimal communication time with minimum consumption of network resources. The main reasons that make so difficult the design of these routing protocols are the highly dynamic nature of VANETs due to the high mobility of the nodes, and the need to operate efficiently with limited resources, such as network bandwidth. Moreover, routing protocols in VANETs, and generally in every Ad-hoc Networks, are not so good in scalability due to frequently changing network topology, lack of predefined infrastructure and limited radio communication range. In the literature, four categories of dissemination routing protocols for VANETs which are presented: position-based, broadcast, geocast and cluster-based.
Broadcast routing is commonly used in ITS applications in VANETS because it guarantees that every vehicle will receive the message. The simplest way to implement a broadcast service is flooding in which each node re-broadcasts messages to all of its neighbors except the one it got this message from. Flooding performs relatively well for a limited small number of vehicles and is easy to be implemented. Furthermore, this protocol is very reliable in safety applications but it consumes high bandwidth and resources, and it can also provoke a broadcast storm when the number of nodes in the network increases. If multi-hop communications are implemented as each node receives and broadcasts the message almost at the same time, this routing protocol generates contentions and collisions and high bandwidth consumption.
However, there are many studies where they use broadcast, but they design an approach to avoid broadcast storm. In [12], Yang et. al propose a V2V communication protocol for Cooperative Collision Warning application. In this approach when a vehicle has an incident, it becomes an abnormal vehicle (AV) and starts broadcasting periodically Emergency Warning Messages (EWMs), with its geographical position, speed and direction to its surrounding vehicles. If this incident provokes that more vehicles have to stop and, therefore, they become also AV, only one of them is going to send the EWMs to avoid the broadcast storm. In [13], Ferrari et. al use broadcasting protocol with multi-hop communication but to avoid the broadcast storm not every vehicle forward the received messages, only the farthest vehicles from the source forward it.
Broadcasting routing protocol
In the position-based routing protocol the forwarding dissemination decisions are based on location information. This approach makes sense because in VANETs the movements of the vehicles are usually restricted in just bidirectional movements constrained along roads and streets, and the geographical location information of vehicles is taken from street maps, traffic models or even more prevalent navigational systems on-board the vehicles. This protocol is commonly used with multi-hop communications and therefore, nodes usually forward the packet to a node that is geographically closest to the destination. The main advantages of this routing protocol are:
It does not require routing tables
Traffic overhead may be small
Supports delivery of packets to a geographical area, called geocasting
For example, as it is shown in Figure 4, if one vehicle has an accident the information will be only be necessary for the vehicles that are behind the damaged vehicle, not for the ones that are not going to drive again though the point the accident has happened.
Position-based routing protocol
However, to use this location-based routing protocol in a built-up city environment is very challenging, due to vehicles are distributed in an irregularly way because they usually are more concentrated on some principal roads than others and the road patterns define their mobility and add difficulty in the signal reception because of the radio obstacles such as high-rise buildings which may lead VANETs unconnected. Furthermore, in general, topology-based routing protocols are considered not to scale in networks with more than several hundred nodes [14].
In order to position-based routing protocol could work, vehicles should send periodically beacon messages to announce their position and enable other nodes to maintain a one-hop neighbor table. This approach is scalable and resilient to topology changes since it does not need routing discovery and maintenance; however, periodic beaconing creates a lot of congestion in the network [15]. This beaconing frequency can be configured according to different scenarios or traffic situations, but if this beaconing frequency is not enough the inaccuracy of position information is higher and a neighbor selected as a next hop may no longer be in transmission range implying to a significant decrease in the packet delivery rate.
Therefore, the key ideas we have to take into account to select one position-based routing protocol are:
Loop-freedom: routing protocols should be inherently loop-free and should avoid recovery strategies using timeouts of old packets and memorizing packets that have been seen before
Distributed operation
Path strategy
Metrics
Memorization
Guaranteed delivery
Scalability
Robustness
There are three different kinds of position-based protocols which are restricted directional flooding, greedy and hierarchical routing protocols. The most used routing position-based protocol is the greedy in which they use forwarding to route packets from a source to the destination. This strategy do not establish and maintain the routes between the source and the destination; on the other hand, a source node define the approximate position of the destination and add this data in the data packet and selects the next hop depending on the optimization criteria of the algorithm; for example, as it is shown in Figure 5, one criteria could be the closest neighbor to the destination [16],[17]. In the same way, each intermediate node selects a next hop node until the packet reaches the destination, as it is shown in Figure 4 Position-based routing protocol.
Greedy routing protocol
The main characteristics of Greedy algorithms are:
Loop free
Localized information
Single path strategy
Metric: Hop count
No memory
No guarantee of delivery
Scalable
Somewhat robust
In restricted directional flooding, the sender will broadcast the packet to all single hop neighbors towards the destination. The node which receives the packet checks whether it is within the set of nodes that should forward the packet (according to the used criteria). If it is, it will forward the packet. Otherwise the packet will be dropped. In restricted directional flooding, instead of selecting a single node as the next hop, several nodes participate in forwarding the packet in order to increase the probability of finding the shortest path and to be robust against the failure of individual nodes and position inaccuracy.
Restricted directional flooding routing protocol
The main characteristics of Restricted Directional Flooding are:
Not loop free
Localized operation
Path strategy: flooding / multipath
Metric: Hop count
Memory
No guarantee of delivery
Not scalable
Not robust
The third forwarding strategy is to form a hierarchy in order to scale to a large number of mobile nodes. This strategy tries to reduce the complexity of the information each vehicle has to handle and also improves the scalability of the network. The two main strategies used to combine nodes location and hierarchical network structures are the zone-based routing and the dominating set routing [18].
Geocast routing is a location-based routing but in a multicast way, so each message is broadcasted to every vehicle inside a defined area. In Figure 7 it is shown that the defined area are the vehicles which receive the yellow messages. Geocast can be implemented with a multicast service by simply defining the multicast group to be the certain geographic region.
Geocast routing protocol
Most geocast routing methods are based on directed flooding, which tries to limit the message overhead and network congestion of simple flooding by defining a forwarding zone and restricting the flooding inside it. With this routing protocol we consume less network resources than broadcast routing but we also guarantee that every vehicle will receive the message. However, we continue having the broadcast storm problem unless we only use one-hop communications. Geocast routing is divided into three types which are: Routing with simple flooding, direct flooding and no flooding [19].
The Geocast routing based on simple flooding was not created for geocast routing but it is used as a basic unit and for the comparison with other protocols. In this method, the source vehicle delivers the packet to all other nodes in the network and all receivers have to check whether they are within the destination area. This is a very straightforward approach but is not a well-organized approach. In this approach, information of location is not used.
In the Geocast routing based on direct flooding the packet is forwarded to a defining region called “forwarding zone”. In this approach a packet is only forwarded to forwarding zone by the source node and not to all nodes in the network. In other words, this protocol is based on flooding but avoids flooding the whole network by defining a forwarding zone, and therefore, outside the forwarding zone the packet is discarded. There are two types of forwarding zone, the first one is the rectangular forwarding zone and the other one is distance-based forwarding zone.
The Geocast routing without Flooding is a simple geocast routing protocol that uses a regular unicast routing protocol between the sender and the destination region. Inside the destination region, flooding can be used, as well as any other routing protocol that can be independent of the protocol used outside the destination region, but the main difference is that it does not use flooding outside the forwarding zone.
But the most used routing protocol for vehicular environment is the cluster-based, where vehicles are grouped into different clusters according to some parameters. These parameters differ from one algorithm to another and are the key factor to build stable clusters. Some of those parameters could be the location, speed or inter-vehicle distance. Other parameters, as the IEEE 802.11p wireless coverage area of each vehicle, could affect in the size of clusters which could vary from one cluster to another in the same network depending on the location of nodes.
Therefore, clusters are virtual groups selected by a clustering algorithm where at least there is Cluster Head (CH) and some Cluster Members (CMs). The main advantage of cluster-based solution is that it can achieve good scalability for large networks, but, on the other hand, the delay and overhead involved in the formation and maintenance of clusters has to be taken into consideration.
The highway, urban, city and intersection scenarios require different characteristics for selection of CHs and for formation of clusters.
Clustering routing protocol
The cluster-based routing solution could be designed in three different ways depending on how vehicles discover the CH. It could be in a proactive, reactive or hybrid way. In the proactive solution beacon messages are constantly broadcast and flooded among vehicles since every vehicle should maintain updated their neighbor table to know which the next hop node toward a certain destination is. The advantage of the proactive routing protocols is that there is no route discovery since route to the destination is maintained in the background and is always available upon lookup. Despite its good property of providing low latency for real-time applications, the periodically beacon sending for the maintenance of the neighbor table requires a significant part of the available bandwidth, especially in highly mobile VANETs.
In the reactive approach the configuration phase is initiated by the vehicle because it starts a communication when it needs to communicate with another vehicle. It maintains only the routes that are currently in use, thereby reducing the burden on the network. Reactive routings typically have a route discovery phase where query packets are flooded into the network in search of a path. The phase completes when a route is found.
In a mixed approach vehicles also send periodic proactive beacon messages to have the neighbor table updated but they are also able to create a new communications on demand when they need to send any message to another vehicle.
To sum up, it is not very obvious which is best routing protocol for data dissemination in VANETs because it depends on application and the characteristics of the scenario like the position of the vehicles, speed, direction of movement, potential communication duration and potential number of communication neighbours, among others. Therefore, research community should continue researching on the development of new dissemination data routing protocols.
Clustering is a technique for grouping vehicles in the geographical vicinity together, making the network more robust and scalable. Under a cluster structure from Figure 9, vehicles may be assigned a different status or function, such as cluster head (CH), gateway (GV), or cluster member (CM). A CH normally serves as a local coordinator for its cluster, performing intra-cluster transmission arrangement, data forwarding, and so on. A GV is a non-CH vehicle with inter-cluster links, so it can access neighboring clusters and forward information between clusters an RSUs. A CM is usually called an ordinary vehicle, which is a non-CH vehicle without any inter-cluster links.
(a) Highway and (b) City scenario.
Cluster-based solutions may be a realistic approach in supporting reliable and scalable multi-hop communication for VANETs [20]. Clustering has been shown to effectively reduce data congestion [26], and can support Quality of service (QoS) requirements [21] for both delay-tolerant (e.g. road/weather information) and delay-intolerant (e.g. safety messages). According to [22] clustering provides three basic benefits.
Spatial reuse of network resources.
Emergence of a virtual backbone.
Improved network stability and scalability from the viewpoint of regular CMs.
Clustering can be done in a centralized or decentralized way. In centralized way, RSU elect CHs and forms clusters based on periodic message. As a fixed infrastructure, the RSU should be fully utilized to collect information and use this information to perform central control. It acts as backbone of all data transmissions. However, it does not work in network where there are no RSUs. Decentralized clustering is based on the ”hello message” exchange between the vehicle and it forms clusters and elects its CHs. Additionally, most protocols only use peer to peer communication to gather and transmit information, so those data can hardly be converged and processed in centralization. This is further discussed in detail in coming section.
Infrastructure based clustering is a centralized clustering where it gathers information from all the vehicles in the road, including speed, direction, positions, and further traffic related information. Infrastructure divides vehicles in the road into different cluster groups, it coordinates in the election of CH, routing of packets and allocation of the channel to its CMs. As a fixed infrastructure, it computes the collected information to perform central control. Moreover, using V2V clustering some algorithms require additional devices for computation to fulfill the aim, which will raise the vehicles cost and reduce the feasibility of algorithms. Infrastructure based clustering is used to solve the above-mentioned shortcomings and to achieve high stability. Overall, the amount of data to be sent is comparatively small (the position, speed, direction of each vehicle), but the communication reliability is vital.
Some approaches shown in Table 2 uses infrastructure for centralized channel allocation in order to reduce channel allotment time and control overhead. It can be seen infrastructure divides the spectrum allocated to a particular area into prefixed overlapping spatial clusters. The medium in each cluster is divided into time slots and each time slot is allocated to a vehicle in accordance to the priority of the message and availability of the time slot. However, due to centralized allotment the reliability and fairness is lowered. In another approach, infrastructure allocates channels to the moving vehicles based on their clusters and enables channel reuse in non-adjacent clusters. The infrastructure broadcast is heard by all the neighboring vehicles in the infrastructure region and this solves the issue of hidden/exposed vehicles. Furthermore, broadcast helps to avoid contention and results in efficient utilization of the allocated bandwidth. The lack of contention for channel acquisition and priority list at the infrastructure allows the protocol to ensure predictable delivery of safety messages. Nevertheless, these types of algorithms may not scale at high density and would not function in ad hoc mode in regions where there are no infrastructures.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
CMAC [23] | \n\t\t\tRSU is CH | \n\t\t\tSpeed, relative distance and direction. | \n\t\t\tFDMA | \n\t\t\tHighway | \n\t\t\tMOVE, SUMO and NS-2 | \n\t\t\tPredictable and reliable. | \n\t\t\tDensity not considered. Require RSUs. Low bandwidth utilization in sparse traffic | \n\t\t
Ranjeet Singh [24] | \n\t\t\tRSU is CH for all clusters | \n\t\t\tStatic cluster formation | \n\t\t\tTDMA | \n\t\t\tIntersections | \n\t\t\tNCTUns | \n\t\t\tEnd to End delay is reduced. | \n\t\t\tRequire RSUs all time. Reliability lowered in high speed scenarios. | \n\t\t
Comparison between various infrastructures based protocols.
Vehicular motion are confined to strait jacket roads and travels at high velocity and the enter/exit infrastructure area in short interim’s of time. At a given period of time, the total number of vehicles in an infrastructure area can vary significantly from a small density of vehicles to a large density of vehicles in a very short interim of time. Algorithms must be distributed or should require partial infrastructure assistance with an efficient hand-off from one infrastructure to another to meet these attributes. The vehicular movement is predetermined to road structure and directional antenna would be suitable for communication via infrastructures. The vehicle broadcast radio frequencies with transmission channels, each one considered as a common medium over which two neighboring vehicles cannot transmit simultaneously because a transmission collision occurs. So, in order to efficiently share the medium, MAC protocol is needed and is beset by contention delay. However, a protocol must ensure that safety messages are delivered within a prescribed time frame. The protocol must not suffer without the hidden/exposed terminal or deafness problem to ensure reliable message delivery. Although the infrastructure is an extra, it will be furnished on the highways extensively and applied in VANET in the near future. Therefore, compared with great and lifelong benefit, the infrastructures expense is of trifling importance at all. The efficient cluster based MAC and routing protocols can provide a more stable communication than a solution using V2V clustering. The optimum protocol should that take the advantages of fixed infrastructure and optimize the problem.
V2V based clustering is a decentralized clustering where clusters are formed based on communication between vehicles. Additionally, the CH election will be based on V2V communication. There are several advantages of using V2V-centric clustering as compared with the infrastructure-centric VANETs. V2V-centric clustering can avoid the short communication link period, high frequent hand-offs, fast channel fading, etc., that are caused by the high relative-speed difference between the fixed infrastructure and the fast-moving vehicles. Finally, the V2V-centric clustering performs better in active safety applications, which only requires exchanging messages among one hop vehicles within their transmission range.
V2V communications are expected to significantly improve transportation safety and mobility on the road. Several applications of V2V communications have been identified, from safety and warning applications, up to traffic control and driver assistance applications. In infrastructure centric clustering, all the communications is done via the infrastructure which causes a lot of control overhead and additional delay. Furthermore, it would be very cost intensive to build an infrastructure based communication all along the road structure. The V2V based clustering technique avoids the use of stationary base stations by building up VANETs, where all vehicles in a common transmission radius can exchange messages. However, CH selection carried out through V2V communications has some shortcomings, e.g., highly complex protocols, large computation and communication cost, need of additional devices and so on. Another important issue is that the connection between two adjacent CHs may be lost due to vehicles high speed, which drastically reduces the link quality. The hidden terminal problem where two vehicles are outside of each other’s transmission radius, but both attempt to transmit to a vehicle that is within the radius of both. This issue is likely in pure V2V scenarios where there is no centralized communication system. The result of the hidden terminal problem is data collisions. By enabling vehicles to transmit/receive messages with each other via V2V as well as with infrastructure communications, VANETs could contribute to more safer and congestion free roads by providing correct and timely message to neighboring vehicles and other related departments.
Clustering can simplify essential functions like bandwidth utilization, routing, and channel access. In MAC layer, it can provide a fairer and reliable channel access to all vehicles in network. This can lead to increase in the reliability of packets and scalability of the network. In network layer, clustering for routing can find the closest vehicles to intended destination. Furthermore, it reduces the number of broadcast and flooding messages in the network. In addition, the overhead for clustering is reduced if the same scheme is used for MAC and routing.
Introducing a cluster scheme already on the MAC layer additionally provides the possibility of a fairer medium access. When clustering applied in VANETs, it brings interesting research point such as broadcast storm that occurs when several vehicles are passing at a specific region at the same time, causing network congestion, packet collisions and delays in the medium access layer. A cluster-based MAC scheme is needed in V2V communication to overcome the lack of specialized hardware for infrastructure and the mobility to support network stability and channel utilization. In this case the CH can take over the responsibility to assign bandwidth to the CMs and therefore even QoS support can be improved. As the bandwidth can be assigned centrally fewer collisions have to be expected which consequently increases the reliability.
Many researchers have proposed cluster based multi-channel medium access control protocols to improve the performance and reliability of VANETs. In these protocols, clustering is used to limit channel contention and provide fair channel access within the cluster. On the other hand, multi-channel is used to increase the network capacity by the spatial reuse of the network resources and reduce the effect of the hidden terminal problem. Moreover, to optimize the communication range and the cluster size is very difficult especially in a highly dynamic environment such as VANETs. However, in order to overcome this situation some approaches divide the service area into a set of region units, and limit the number of vehicles in each region unit for the contentions of radio channels. Each region unit is then associated with a non-overlapping radio channel pool. Since the number of vehicles in each region unit is limited, the contention period is reduced and the throughput is increased. However, these types of approach have low bandwidth utilization in case of sparse traffic. Some of clustering algorithms try to minimize the total number of clusters by creating hierarchical clusters with a diameter of at most four hops. In this section of the chapter, we compare well known cluster based MAC protocols in Table 3.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
HCA [25] | \n\t\t\tMaximum number of messages received from cluster relays is the CH. | \n\t\t\tMaximal distance between a CH and CM is two hops. | \n\t\t\tTDMA | \n\t\t\tCity | \n\t\t\tOMNeT++, SUMO | \n\t\t\tEnd to End delay reduced. | \n\t\t\tOverhead and packet loss is increased. Do not consider the direction of movement. | \n\t\t
Zaydoun [26] | \n\t\t\tVehicle nearer to middle of the cluster is the CH. | \n\t\t\tNot Specified | \n\t\t\tTDMA | \n\t\t\tCity | \n\t\t\tC++ with graphical interface. | \n\t\t\tSupport both safety and non-safety applications. | \n\t\t\tNot suitable for high traffic. High overhead. | \n\t\t
Xi Zhang [27] | \n\t\t\tNo reception of a message longer than a particular time units from a CH, then it elects itself as CH. | \n\t\t\tRSS > threshold. | \n\tTDMA in CMs- CHs, CSMA/CA CHs- CHs | \n\tHighway | \n\tSimone 2000 | \n\tReduces data-congestion and supports safety messages. | \n\tHigh overhead and complex algorithm. Require two transceivers | \n
CBMAC [28] | \n\tCH is based on waiting period of Hello messages to neighbors. | \n\tUndecided state to CM based on reception of one CH messages. | \n\tTDMA | \n\tCity | \n\tI-V Communication Based on Traffic Modeling. | \n\tMinimizes the hidden terminal problem. | \n\tDoes not select a stable CH during initial CH election. | \n
RCM [29] | \n\tNo CH | \n\tGeographical area. Vehicles are assigned to different channel pools. | \n\tTDMA | \n\tHighway | \n\tA. law el al | \n\tReduced contention and throughput is increased. | \n\tLow bandwidth utilization in sparse scenarios. | \n
TCMAC [30] | \n\tLane weight, average distance, maximum number of neighbors, and average distance level. | \n\tNot specified | \n\tTDMA | \n\tHighway | \n\tNs-3 | \n\tChannel utilization, scalability, avoids hidden terminal problem, decreases collisions and packet drops. | \n\tCannot be used for safety applications, it is delay intolerant. | \n
CFIVC [31] | \n\tRandom after relaying one packet to ordinary node. | \n\tAccording to speed. | \n\tCDMA, MCSCDMA | \n\tNot simulated | \n\tNot simulated | \n\tAvoids data collisions. | \n\tIt neglects any condition that might affect the maximum speed achievable by the vehicle nodes. | \n
Comparison between various cluster based MAC protocols.
The MAC layer is divided into different cycles and each cycle is divided into contention based or contention free. In the current literature, several MAC protocols have been proposed to VANETs. Some of the well-known MAC protocols are ADHOC MAC [32], SDMA [33], VeMAC [34], DMMAC [35], STDMA [36], VeSOMAC [37] etc. These protocols are proposed for various scenarios and have many drawbacks such as hidden terminal problem, time unbounded, unreliability etc. There is a need for new MAC protocol in VANETs that can provide mobility (i.e., the MAC protocol should support vehicles to leave and join inter-vehicle communications at high speed), delay bounded (i.e., the communication must be delay bounded and real-time), scalability (i.e., VANET should scale itself according to the number of vehicles present), bandwidth efficiency (i.e., the radio resource should be utilized in an efficient and fair manner), cost (i.e., for cost-efficient and reliable communications, VANET should be fully decentralized), and fairness (i.e., every vehicle should get a fair chance to get the radio channel).The challenge of successfully deploying VANET services is to ensure timely and reliable data delivery for mobile vehicles.
In network layer clustering, a virtual network infrastructure must be created through the clustering of vehicles sharing similar characteristics in order to provide scalability. Routing protocols for VANETs mostly based on periodical broadcast messages to reveal their positions and traffic information to neighbors. Nevertheless, deterioration of routing performance is anticipated in urban areas due to high density of vehicles in the network. Basically, excessive broadcast messages as well as broadcast overhead may increase, resulting packet losses (due to collision) and significant routing performance deterioration. Information transfer or dissemination needs multi hop communications. When exchanging information between vehicles, there are network issues that must be addressed, including the hidden terminal problem, high density, high node mobility, and data rate limitations.
In multi-hop data forwarding method, the key problem is selecting the relay/CH for data routing. Most of the relay/CH selection method presented is more suitable for highway scenarios. In a city environment, the widely adopted method is the store-carry-forward scheme. Reactive protocols find routes on-demand. If a node wants to communicate with a node to which it has no route, the routing protocol will try to establish the shortest route between them. Here there is significant delay in determining the route. Proactive (table-driven) protocol, which is based on the exchange of control packets and it is continuously updating the reachability information in the routing table, so routes are immediately available when requested but there is high overhead in maintaining updated periodic routing tables and also maintains the routes that are not going to be used. Hybrid protocol is combination of proactive and reactive protocol. It is also known as cluster based routing. It is a convenient way for developing efficient routing scheme in VANETs. In Table 4 we compare between various cluster based routing protocols proposed in VANETs.
\n\t\t\t | \n\t\t\n\t\t\t | \n\t\t\n\t\t\t | \n\t\t\n\t\t\t | \n\t\t\n\t\t\t | \n\t\t\n\t\t\t | \n\t
TMRC [38] | \n\t\tDirection of vehicle after crossing the intersection | \n\t\tIntersection | \n\t\tNCTUns | \n\t\tComputed optimal length of cluster in an intersection. | \n\t\tCluster overheads and delays are increased. | \n\t
RMAC [39] | \n\t\tSpeed, location, and direction | \n\t\tHighway | \n\t\tNs-2 | \n\t\tStable and less cluster reconfigurations | \n\t\tCollisions and unreliable. | \n\t
VWCA [40] | \n\t\tNumber of neighbors, the direction, the entropy, and the distrust value parameters | \n\t\tNot simulated | \n\t\tNot simulated | \n\t\tPredictability and reliability is increased. | \n\t\tVolatility of dynamic transmission range | \n\t
MOBIC [41] | \n\t\tVariance of relative mobility with each of its neighbour’s. | \n\t\tRandom | \n\t\tNs-2 | \n\t\tReduces the cluster reconfiguration by delaying re-clustering for a certain period of time. To avoid accidental contacts between CHs. | \n\t\tFew neighbour nodes move differently, the method still results in dramatic increase in the variance. | \n\t
AMACAD [42] | \n\t\tRelative distance, speed between neighbour’s and distance between vehicle and destination. | \n\t\tUrban | \n\t\tJava JDeveloper 10G | \n\t\tIncreases the cluster and CH lifetime. | \n\t\tProblem with knowing the final destination a priori as drivers usually do not use navigation system for known routes. | \n\t
MCDRIVE [43] | \n\t\tFirst vehicle in the direction is elected as CH | \n\t\tIntersections | \n\t\tNCTUns | \n\t\tCluster stability is improved in intersections. | \n\t\tIncreased overhead and delay. | \n\t
APROVE [44] | \n\t\tMinimum distance and minimum relative velocity between each CH and its CMs. | \n\t\tHighway | \n\t\tNs-2, VanetMobiSim | \n\t\tCluster overhead and re affiliation are reduced | \n\t\tIt doesn’t consider destination of vehicles. Not mention about CH election. Not suitable for intersections | \n\t
ALM [45] | \n\t\tVariance in relative mobility | \n\t\tBox topology | \n\t\tSUMO, SIDE/ SMURPH | \n\t\tConsiders relative mobility to increase stability. | \n\t\tNo direction of movement and position is considered for cluster formation. Overhead increased. | \n\t
DBC [46] | \n\t\tConnection graph density, link quality, traffic conditions, node reputation and movement prediction. | \n\t\tUrban | \n\t\tJiST/ SWANS++ VanetMobiSim | \n\t\tSuitable for both sparse and dense traffic. | \n\t\tThe destination of vehicles, speed of vehicles is not taken into account that increases the overhead. | \n\t
Maslekar [47] | \n\t\tLocation and direction of vehicles. | \n\t\tIntersections | \n\t\tNCTUns | \n\t\tCluster stability is improved in intersections | \n\t\tOverhead and delay increased. | \n\t
Maslekar [48] | \n\t\tThe direction which the vehicle will take after crossing intersection. The CH is at the front of cluster. | \n\t\tIntersections | \n\t\tNCTUns | \n\t\tImproved the influence of overtaking within the clusters. accurate density estimation within the clusters. | \n\t\tOverhead and delay increased. | \n\t
Comparison between various cluster based routing protocols.
CH selection is important to increase protocol reliability, scalability and delay. In some of CH selection algorithms proposed takes into account the destination of vehicles, including the current location, speed, direction, relative destination and final destination of vehicles as parameter to arrange the clusters. Many researchers have proposed CH election scheme based on ID. Each node is assigned a unique ID, and the node with the lowest ID in its two-hop neighborhood is elected to be the CH. Some algorithms calculate these ID based on the variance of relative mobility of a mobile node with each of its neighbors, where a small value of variance indicates the mobile node is moving relatively less than its neighborhood. Additionally, other approaches consider vehicles having a longer trip are more qualified for being elected as CHs.
A vehicle, which would travel longer time, is assigned higher priority; hence, at the very beginning of starting its travel, the expected travel time of a vehicle is calculated and announced using its desired driving speed and the geographic information system once its driver sets the destination. Te stability of the system is improved by electing the vehicles having a longer trip as the CHs. Furthermore, to avoid elected CHs losing connectivity with their neighbors very soon, the eligibility of a vehicle should decrease quickly when its velocity has big difference from the average speed. Thus, a vehicle with large speed deviation is assigned lower priority.
Another type of CH election scheme is based on connectivity level (estimating graph density), link quality (SNR), relative node position and the prediction of this position in the future, and node reputation. The vehicle which is near to that anchor point is elected as CH. Furthermore, some approaches assign generic weight to vehicles based on the position and other set of vehicle parameters like connectivity, mobility, RSS etc. The vehicle with the highest weight is elected as the CH amongst the neighbors. However, since the vehicles are highly dynamic in nature the position of the vehicles change very fast and hence may induce a computational overhead in calculating the weight associated with the vehicles.
In some of the clustering algorithm first vehicle entering into the cluster region is initialized as the CH. It changes from CH to CM due to the discovery of a closer CH, or until the last member of the cluster passes the intersection. However, CH stability is reduced due to distance between vehicle to intersection and due to different directions of vehicles. In some other schemes, the CH selection should resemble like a natural model of location references. CH re-election only occurs when two CHs move within range of one another for a certain contention interval. When a CM moves out of range of its CH, it joins any current CH in its neighborhood, or forms a new cluster. However, in the case in which few neighbor nodes move differently, the method still results in dramatic increase in the variance.
Cluster formation is really important to avoid cluster reconfigurations. Some of the cluster formation techniques are based on position based clustering. In these types of protocols, each road is divided into cells and in each cell some anchor points are defined. The cluster structure is determined by the geographic position of the vehicle. Another type of position based algorithm is based on hierarchical and geographical data collection and dissemination mechanism. The cluster formation is based on the position of the vehicles at a particular segment instead of the individual positions. However, this type of protocols incurs more overheads for V2V and V2I communication. In some other approaches, each vehicle entering into the network collects the neighbor vehicles information, assuming precedence to each vehicle and polls each vehicle individually (according to precedence) to check whether it is CH or not and then joins the cluster. Also every vehicle in the network collects 2-hop neighbor’s information along with 1-hop neighbor’s information from the CH through periodic polling. These two information collection leads to more overhead in V2V communication.
Some clustering algorithms estimate the future mobility of nodes predicting the probability that the current neighborhood of a mobile node will remain the same. The drawback of the prediction method is the lack of accuracy in some cases. In some of clustering algorithms, the clusters are formed based on mobility metric and the signal power detected at the receiving vehicles on the same directed pathway. Through such method this type of protocol helps in forming stable clusters. However, it does not consider the losses prevalent in the wireless channel. In practical scenario effects of multi path fading are bound to affect the cluster formation method and thus the stability. These effects of multi path fading are taken into account in the density based clustering algorithm. The cluster formation is based on the weight metric which takes into consideration the link quality and the traffic conditions. It can be seen that the stability is improved compared to other approaches.
In some clustering approaches considers the behavior of the vehicles, using the speed and direction parameters.
The cluster formation is based on direction of vehicle at the approaching intersection. In other approach, cluster is formed based on distance and direction of vehicle it takes after crossing the junction. Some of the research enforces a weight cluster mechanism with a backup manager. These algorithms operate in similar way. Algorithms consider the position, direction, speed and range of the nodes to perform the algorithm. On the other hand, some takes into consideration the number of neighbors based on the dynamic transmission range, the direction of vehicles, the entropy, and the distrust value parameters. They works with an adaptive allocation of transmission range (AATR) technique, where hello messages and density of traffic around vehicles are used to adaptively adjust the transmission range among them. The destination of the vehicles is used as a parameter to arrange clusters.
In some approaches, the cluster formation interval is constant, which implies a synchronous creation of clusters. This does not allow for effective cluster reorganization. The directional based clustering algorithm are based on the following mobility metrics (a) moving direction (b) leadership duration (c) projected distance variation of all the neighboring vehicles over time. In practical scenario effects of multi path fading are bound to affect the cluster formation method and thus the stability. Some approaches take into account the destination of the vehicles to arrange the clusters and implements an efficient message mechanism to respond in real time and avoid global re-clustering. There might be a problem with knowing the final destination a priori as drivers usually do not use navigation system for known routes. Some algorithms are proposed for calculating the density of vehicles in a particular region around the junction. Moreover, other algorithms groups vehicles into clusters based on the competitive learning Hebb neural network. A suitable solution to prolong the cluster lifetime, stability, fairness, avoid congestion and overhead considering the vehicular behavior is essential.
One of the numerous challenges clustering algorithms in VANETs is the mobile and dense communication topology. The main problem in clustering is the control overhead introduced to elect the CH and to maintain a stable cluster. The cluster structure assures the scalability of VANETs, where high mobility of the moving vehicles within the road causes lots of challenges to face. Location services might not provide the needed accuracy everywhere or will not be available at all so more work is needed on location independent clustering solutions. Providing highly accurate digital maps that are needed by some solutions presents a challenging task and could slow down the implementation so advantages and disadvantages of map based solutions should be researched.
In many papers the correlations between the transmission range and the VANETs density, packet transmit rate, packet size, data rate and channel conditions have been researched. However, the different network simulators should also be evaluated and presented with all the relevant parameters including MAC, transmission range, packet size, bit rate etc. Since each vehicle in the VANET has its own view of the network density and channel conditions, finding the optimal network parameters is difficult. The research should focus to the optimization of cluster size and transmission range that maintains a high VANET stability and reliability, increases the life time of a connection link, and at the same time decreases the time required for a safety message to reach its intended destination. Presented clustering protocols are designed for different aims e.g. overhead minimization, fast cluster creation, cluster stability, etc. The most important parameter among them is the cluster stability. Their tradeoffs and effects between them should also be analyzed and presented.
The vehicles with relatively high mobility, can pose difficulties for flat networks stability. Many of the presented protocols use metrics derived from the same input parameters where among them position and radio signal strength(RSS) are the most important. More research effort should be put in defining and ranking the aims that clusters and clustering protocols should try to achieve. One of the goals is to optimize the mean number of created clusters and the number of CHs at each time step.
For performance evaluations of clustering protocols common parameters used are cluster stability, CH election, cluster size, cluster delay, cluster reconfiguration and cluster overhead etc. These terms are quite generic so their definition and explanation with VANET specifics is needed to provide consistency between different researches. More focus should be put on evaluation of those common parameters to highlight the most useful ones, merge similar ones etc. This would help researchers to concentrate their research on extending and designing the most prospective ones. Fair comparison of different clustering protocols is a hard task due to non-existent standard testing procedures and scenarios so more work and standardization is needed in this area. The characteristics of different scenarios of VANETs and different parameters are explained in detail in later sections.
In highway scenarios, it is widely recognized that traffic generally follow a platoon pattern according to traffic flow theory. Vehicles in a platoon generally move with similar velocities and are likely to sustain a stable wireless communication in clusters. The clusters are independently controlled and dynamically reconfigured as the vehicles moving. Congestion can occur in highways during an accident so the clustered protocols should be designed to effectively reduce data congestion in high density scenarios, and satisfying QoS requirements. Furthermore, the design of cluster protocol should also consider the market penetration of vehicles enabled with OBU´s. In some cases, there can be a large number of vehicles in road that are not enabled with OBU´s. This creates a large gap between vehicles and resulting in poor communication. The future clustering protocols should consider all the characteristics of highway scenarios.
A large number of the available cluster based MAC and routing protocols are purposed for highway environments and does not address the various requirements of the city and urban traffic environments. In city environments, intersections play important roles for information exchange. The vehicle that crosses the intersection before actually receiving a message is defined as the unstable vehicle. As the intersection area is comparatively small and the probability of change of direction is very high, it will be risky to choose an unstable vehicle as the CH from these clusters. Moreover, during rush hours of day intersections are usually the bottlenecks.
Vehicles in intersection can take any of the direction Straight (S), Right (R), Left (L) and U-Turn (U) respectively. All the incoming vehicles of two road segments of intersection may be blocked by the red signal, whereas vehicles on the other two road segments flow until the green signal is on. When a vehicle crosses the intersection without having another vehicle arrive at the intersection, a disconnection may occur. Such a situation arises only when a fleet of vehicles has crossed the intersection and when another fleet of vehicles has not been arrived at the intersection. Based on the motion of vehicles, some approaches form clusters S, R, L and U on a particular lane. The created clusters consist of vehicles moving in the same direction. Within the same cluster the vehicles communicate with each other and elect a CH that is responsible for calculating the number of vehicles in its cluster. This information will help to avoid constant cluster reconfigurations and overhead by creating another cluster.
For intersection collision avoidance, the amount of traffic generated by vehicles can be determined by a number of factors such as the cluster size, the number of intersection per cluster, the number of vehicles per intersection per cluster, the size of messages, and the transmission interval.
The size of the cluster is a crucial parameter. To optimize the cluster size is very difficult especially in a highly mobile environment such as VANETs. One of the goals of optimal protocol is to optimize the number of CMs to decrease the end to end delay of messages. If the cluster size is decreased, the channel contention within each cluster decreases. However, the number of CHs is increased, so that the resulting virtual network formed by these CHs will become more complex. There is then a tradeoff between the cluster size and the number of CHs. Cluster size is variable according to vehicle density, speed and required minimum bandwidth or QoS where parameters can be predefined or provided on the fly from vehicle sensors and application profiles.
The cluster size can be controlled by a predefined transmission range between a CH and its CMs. Optimal cluster size and hence the transmission range that maintains a high VANET reliability, stability and scalability, increases the life time of a communication link, and at the same time reduces the end to end delay for a safety message to reach its intended destination. Optimal cluster size is both related to the radio transmission power and vehicle traffic density. Therefore, cluster size may limit radio efficiency and throughput. For the cluster protocols, we have so far assumed that transmission power is fixed and is uniform across the VANET. There are different methods to reduce the cluster size by reducing transmission power. There is different power control protocols proposed but most of them are oscillating because of the fast varying vehicles densities in VANETs. Selection of optimal power control algorithm and vehicular densities will reduce the end to end delay, reliability and fairness.
Optimal cluster size is also determined by the correlation between spatial reuse of the medium (which leads to small numbers) and end to end delay minimization (which lead to large numbers). Other parameters also apply, such as geographical area and power consumption.
Stable clusters are important for a reliable and efficient information exchange. Stable clustering techniques decrease the control overhead of cluster reconfigurations and led to an efficient hierarchical VANET topology. The main condition for stability is the duration of residence´s times of a cluster and its CHs. Stability is also defined by long CH lifetime, and long CM lifetime.
Cluster stability is based on the selection of suitable CMs to ensure greater cluster lifetimes by reducing cluster re-configuration events. Cluster stability also depends on the different vehicle densities. To be able to form stable clusters of one hop vehicles, vehicular movements should be taken in to account. Speed and location data transfer is a usual procedure in most of the cluster-based routing protocols. Nevertheless, this needs two additional communication rounds (for speed, location and relative stability data transfer) and stationary assumption of vehicles prior to cluster creation. Cluster stability can be defined as the average number of cluster changes throughout the simulation and the percentage of time in which vehicles were CMs, represented as association time. In practical environment effects of multi path fading are bound to affect the cluster creation method and thus stability. In some cases, nodes in cluster are linked to cluster rather than CH. This increase furthers the cluster stability.
The time during which a node is in the state of a CH determines stability. It is the mean time duration of the nodes, remaining its leadership role as CHs. Long CH lifetime implies, few changes and good stability. The information is disseminated by groups enhancing the communication delay, reliability, low data delivery and congestion issues, making the vehicular networks accurate and efficient. CH stability can be affected by different factors such as merging, distance between CHs, exit from the road etc. In VANETs, merging collisions can happen among vehicles moving in the same direction due to acceleration or deceleration, it is more likely to occur among vehicles moving in opposite directions (approaching each other) or between a vehicle and a stationary RSU since they approach each other with a much higher relative velocity as compared to vehicles moving in the same direction. The high mobility of the shifting nodes within the networks causes lots of challenges to face and affects stability.
If vehicles are changing their state very often in intersection scenarios and stay only for short times in the CH state, CH stability is low. In some of intersection based approaches the first vehicle to enter the intersection region in a particular direction is elected as CH to improve stability. Furthermore, some cluster-based routing algorithms, the selection of CH are based on willingness factor which defines the relative stability of a node. CH stability is also based on the threshold distance between the two CHs. Optimal distance between two CHs should be obtained.
Cluster delay means the time required for sending one message from source to destination (it can be here from CM to the RSU or vice versa). The delay parameter is very crucial for safety applications. The end to end delay can be minimized by selecting proper cluster size, selection of proper MAC protocol to reduce the channel access time, selection of stable CH nearer to the RSU, a selection of proper routing algorithm between CH transmissions. The number of the formed clusters is important to reduce the end to end delay for message transmission.
The frequent cluster reconfiguration generates tremendous communication load, which significantly reduces available bandwidth for message dissemination. Cluster reconfiguration is needed in some cases when the CH leaves the group or numbers of CMs are below the threshold or the distance between two CH is below the threshold. In some approaches, if the distance between two CH nodes is detected less than the particular threshold, the cluster with fewer CMs is dismissed to reduce communication overheads while it’s CMs join other clusters. One can expect that a larger dismiss threshold leads to a higher rate of CH changes and higher probability of cluster reconfiguration. The threshold determines the rate of cluster reconfiguration, and also, depends on the radio transmission range and vehicular densities. Larger transmission provides longer distance for CHs to detect each other, and therefore, more frequent cluster reconfigurations occur. Additionally, some algorithm elects backup CH to avoid cluster reconfigurations. However, most of the protocols are not fit for different traffic situations. The aim should be to design protocol with less cluster reconfiguration in various scenarios.
Data rate transfer that gives the total number of received packets at the destination out of total transmitted packets. An access collision happens when two or more CHs within two hops of each other attempt to acquire the same available time slot.
Clustering requires explicit clustering-related information exchanged between node pairs. Clusters cannot be formed or maintained by non-clustering-related messages, such as routing information or data packets. The main challenge in clustering is the communication overhead introduced to formation and maintenance of a stable cluster, and elects its stable CH. Most of the recently proposed protocols discuss mainly on how CHs are selected. The control overhead for the creation and reconfigurations of clusters have not been considered completely. There have been not many papers that analyze analytically the control overhead incurred in hierarchical routing. Furthermore, the overhead is bound by a constant per vehicle per time step, avoiding expensive re-clustering chain reactions; hence, this overhead increases with the number of nodes. Since a CH acts as a coordinator in a cluster, if it is absent for any reason, the clustering architecture has to be reconfigured; this will significantly increase the message overhead.
Communication complexity represents the total amount of clustering-related message exchanged for the cluster formation. For clustering schemes with ripple effect, the communication complexity for the re-clustering in the cluster maintenance phase may be the same as that in the cluster formation phase. But for those with no ripple effect, the communication complexity of re-clustering should be much lower. From analysis of different clustering protocols, we believe that a more efficient way to form a stable network structure, with reduced overhead, are that a vehicle should be associated to a cluster and not to a CH. Indeed, replacing CH is considered only as an incremental update and does not require a whole reconfiguration of the cluster structure; this will definitively increase the lifetime of the clustering architecture. The resulting clusters are stable and exhibit long average CM duration, long average CH duration, and low average rate of CH changes. The cluster creation and maintenance overhead should be calculated to be compared with non-clustering algorithms in terms of the reliability, fairness, and scalability of the algorithms. By optimizing cluster stability, cluster reconfiguration, number of clusters and cluster size can reduce the overhead caused in clustering.
In this chapter, we have surveyed in-depth of the challenges of reliable communication for cooperative ad hoc networks especially VANETS. First we have provided state of the art of ad hoc networks and various types of ad hoc networks.
In a scenario where nodes are moving fast and the topology of the network is changing continuously, the big challenge is to keep connected all the nodes and give all of the them resources to transmit and receive information in real time. In VANETs, dissemination algorithms provide to the drivers mechanisms to be aware in real-time of events that are happening in their surroundings: traffic and road conditions, closure and detour information, incident information, emergency alerts, and driver advisories. Clustering is an approach that divides the nodes of the network in groups of vehicles according to common characteristics as their position or speed, in order to create a more robust and scalable network. This structure can be a realistic approach to support reliable and scalable multihop communications in a mobile network as a VANET
This chapter focused on identifying the research trend of the cluster based MAC and routing techniques that have been recently proposed for V2I and V2V communications. Furthermore, we discussed the advantages and disadvantages of various MAC protocols that have been developed recently. Moreover, we have presented a comprehensive review of the cluster based routing protocols for inter-vehicle and vehicle-to-infrastructure communication. Cluster based routing protocol is the most appropriate technique for developing reliable, scalable and predictable routing protocols in VANETs. However, due to the distinctive attributes of V2V and V2I communications, it raises several open issues and areas for research, such as fairer usage of network resources and channel access. Because of varying vehicle density and varying speed of vehicles makes communication reliability, a challenging issue.
Our research group is focusing on developing data dissemination and cluster based protocols by identifying common characteristics and parameters to improve cluster lifetime, communication link among vehicles, and channel access. We have also discussed some important issues that must be addressed for safety and non-safety applications. Future protocols need to effectively consider these problems while fully exploiting the distinctive distributive and ad hoc nature of these networks to meet real time applications.
The authors would like to thank the EU Intelligent Cooperative Sensing for Improved traffic efficiency (ICSI) project (FP7-ICT-2011-8) for its support in the development of this work.
The governing equations of physical, biological and economical models often involve features which make it impossible to obtain their exact solution. For instance, problems where we observe “a complicated algebraic equations”, “the occurrence of a complicated integral”, in case of differential equations (DE), “a varying coefficients or nonlinear term” sometimes problems with an awkwardly shaped boundary are tough to solve with the limited methods for finding analytical solutions. The main purpose of this chapter is to describe the application of perturbation expansion techniques to the solution of DE. Approximate expressions are generated in the form of asymptotic series. These may not and often do not converge but in a truncated form of only two or three terms, provide a useful approximation to the original problem. These analytical techniques provide an alternative to the direct computer solution. Before attempting to solve these DE numerically, one should have an awareness of the perturbation approach. An example of this occurs in boundary layer problems where there are regions of rapid change of quantities such as fluid velocity, temperature or concentration. Appropriate scaling of the boundary layer dimension is required before a numerical solution can be generated which will capture the behavior in the rapidly changing region.
When a large or small parameter occurs in a mathematical model of a process there are various methods of constructing perturbation expansions for the solution of the governing equations. Often the terms in the perturbation expansions are governed by simpler equations for which the exact solution techniques are available. Even if exact solutions cannot be obtained, the numerical methods used to solve the perturbation equations approximately are often easier to construct than the numerical approximation for the original governing equation.
First, we consider a model problem for which an exact solution is available against which the perturbation expansion can be compared. A feature of the perturbation expansions is that they often form divergence series. The concept of an asymptotic expansion will be introduced and the value of a truncated divergent series will be demonstrated.
This example studies the effect of small damping on the motion of a particle. Consider a particle of mass
Integrating (1), we obtain the solution
On defining the non-dimensional velocity
with the solution
Taking account of the air resistance, and is included in the Newton’s second law as a force dependent on the velocity in a linear way, we obtain the following linear equation
where the drag constant
Let us denote the dimensionless drag constant by
where
It is possible to solve (6) exactly since it is of variables separable form. Here, we solve by an iterative process, known as perturbation expansion for the solution.
Let
The justification for this iterative scheme is that the term
The first iterate is obtained by neglecting the perturbation, thus
This is known as the unperturbed problem, and direct integration yields
The next iterate
and integration yields
Similarly,
Direct integration yields the solution
Rearranging the terms in these iterates in ascending powers of
Clearly as the iteration proceeds the expressions are refined by terms which involve increasing powers of
An alternative procedure to that of developing the expansion by iteration is to assume the form of the expansion at the outset. Thus, if we assume that the perturbation expansion involves the standard asymptotic sequence
The coefficients
Thus, the coefficients of powers of
The proof of validity of this fundamental procedure can be developed by first setting
This is valid for all nonzero values of
Integrating the equations in (11), we obtain
Using these values in (9), we obtain that
This is the same as the expansion (8) which is generated by iteration.
The IVP (6) can be solved exactly as
The perturbation expansion can be obtained from (12) by replacing the exponential function by its Maclaurin expansion, i.e.,
This is the same as the expansion (12). Thus, the perturbation expansion approach is justified in this case. One can refer the books [1, 2].
The letters
If a function
The functions are said to be of the same order as
For example, we have the following functions:
The expression
means that
We have the following functions satisfy the
Consider the expansion
is an asymptotic expansion as
The following expansion is used when (17) and (18) hold,
Here,
The sequence
Some examples of asymptotic sequences are
The general expression for an asymptotic expansion of a function
where the coefficients
where
If a function possesses an asymptotic expansion involving the sequence
Consider a function
The coefficients of the gauge functions
Refer [3, 4] for more details. For (24) to be a uniform asymptotic expansion the ultimate proportionality between
for
An example of a uniform asymptotic expansion is
An example of a nonuniform expansion is
Here, one cannot find a fixed
The expansion (27) becomes nonuniform when subsequent terms are no longer small corrections to previous terms. This occurs when subsequent terms are of the same order or of dominant order than previous terms. Subsequent terms dominate previous terms for larger
The critical case is such that subsequent terms are of the same order. This determines the region of nonuniformity. In (27), the region of nonuniformity occurs when
There are two common reasons for nonuniformities in asymptotic expansions, they are
Infinite domains which allow long-term effects of small perturbations to accumulate.
Singularities in governing equations which lead to localized regions of rapid change.
Consider the nonlinear Duffing equation
Suppose the solution may be expanded using the standard asymptotic sequence
On substituting this in (28) and in the initial conditions, we get
Equating like of powers of
and
Solving Eqs. (30) and (31), we obtain
The term
The trigonometric functions are treated as
The second common source of nonuniformities is associated with the presence of singularities. Consider, the following initial-value problem:
where
Substituting (34) in (33), we have
Equating coefficients of like powers of
Clearly,
but the initial condition
The unperturbed problem, obtained by setting
Thus, the perturbation expansion (36) is a good approximation of the exact solution away from the region
The perturbation expansion (36) generates the second member of (37), but not the first member. The coefficient
Boundary layers are regions of nonuniformity in perturbation expansions of the form (36).
Boundary layers are regions in which a rapid change occurs in the value of a variable. Some physical examples include “the fluid velocity near a solid wall”, “the velocity at the edge of a jet of fluid”, “the temperature of a fluid near a solid wall.” Ludwig Prandtl pioneered the subject of boundary layer theory in his explanation of how a quantity as small as the viscosity of common fluids such as water and air could nevertheless play a crucial role in determining their flow. The viscosity of many fluids is very small and yet taking account of this small quantity is vital. The essential point is that the viscous term involves higher order derivatives so that its omission necessitates the loss of a boundary condition. The ideal flow solution allow slip to occur between a solid and fluid. In reality the tangential velocity of a fluid relative to a solid is zero. The fluid is brought to rest by the action of a tangential stress resulting from the viscous force.
Mathematically the occurrence of boundary layers is associated with the presence of a small parameter multiplying the highest derivative in the governing equation of a process. A straightforward perturbation expansion using an asymptotic sequence in the small parameter leads to differential equations of lower order than the original governing equation. In consequence not all of the boundary and initial conditions can be satisfied by the perturbation expansion. This is an example of what is commonly referred to as a
Consider the following two-point boundary value problem:
where
then the equations associated with powers of
and the boundary conditions require
which leads to
Equation (42) require that each
The general solution of (42) is
From (42), we obtain the equations
and its solutions are
Therefore, the outer expansion is
where ‘out’ label is used to indicate that the solution is valid away from the region near
The exact solution of the BVP (38) can be obtained as
The constants
We know that
and the exact solution is
after rearranging the terms in asymptotic order, we obtain
Comparing the exact solution with the outer expansion shows that the terms involving
The behavior of the exact solution and the zeroth-order term of the outer expansion are plotted in Figure 1 for various values of
Exact solution of
By differentiating the leading order term
Outside the boundary layer, i.e., for
This indicates that
so that within the boundary layer
The variable
We assume a boundary layer expansion, called the
The inner expansion will satisfy the boundary condition at
with solutions
The boundary condition at
The leading order terms in the ‘inner’ and ‘outer’ expansions are to be matched at the ‘edge of the boundary layer’. Of course there is no precise edge of the boundary layer, we simply know that it has thickness of order
Equating at
If, instead we choose to match at
These two expressions differ in the argument of the exponential and differ algebraically with
where the remainder is uniformly
The limit
Applying these conditions to the current example leads to
which yields
To prove that these are valid leading terms we consider
We conclude that the matching condition has correctly predicted the leading order terms.
As single composite expression for these leading order terms can be constructed using the combination
where
For the current example,
Prandtl’s matching condition can only be used for the leading order terms in the asymptotic expansions.
The outer, inner and composite expansions of the BVP (38) are presented in Figures 2 and 3 for different values of
Outer, inner and composite expansions. (a) For ε = 0.2; (b) For ε = 0.1.
Outer, inner and composite expansions. (a) For ε = 0.05; (b) For ε = 0.025.
Consider the following linear DE
The following general statements can be made about the boundary layer location and the nature of the inner expansion.
The solution of this equation is
where
The boundary layers which we have met so far have all had thickness
There are practical situations where the boundary layer thickness will be of
Consider the example
Since the signs of the first and second derivatives are the same, and the boundary layer will occur at
The one-term outer expansion
To determine the inner expansion we first wrongly assume that the boundary layer thickness is
If the appropriate stretching transformation has been used for the boundary layer then
The solution is
Thus, we reject the assumption of a boundary layer of thickness
Next, suppose that the boundary layer thickness is
Again we argue that if the appropriate stretching has been used then all derivatives are of
The solution is
The correct choice of stretching transformation is
The dominant equation satisfied by
The solution is
which leads to
The one-term composite expansion is
The leading order boundary layer equation associated with the stretching transformation
The composite expansion (62) can be verified by comparing with the exact solution of (56). The general solution of (56) is
where
We expand
so that
Using the boundary conditions and by neglecting the transcendentally small term
There is an apparent discrepancy between (64) and the composite expansion (62) in the coefficient of the
Consider the following two-point BVP:
We seek a one-term composite expansion for the above BVP. We will tentatively assume that a boundary layer occurs at
The one term outer expansion satisfies
Its exact solution is
Let us assume that the boundary layer thickness is of
The second-term is always dominated by the third, so the principle of degeneracy requires the first term to be of the same order as the third term (i.e.,
The solution of the above problem is
which yields
The on-term composite expansion is
We conclude this example with the observation that a choice for the value of the index
Thus, if
which gives
Consider the BVP:
The coefficient of the first derivative (convective term) is positive in
with the solution
The outer expansion for negative
with the solution
We suppose the boundary layer at
The third term is dominated by the second term. The first term has the same order as the second term if
Its solution can be given by
Prandtl;s matching condition applied to the region
and corresponding for
Using the limiting values
A composite expansion cannot be formed in the standard way when there is more than one outer solution. However, the behavior of
Utilizing this enables a uniformly valid one-term composite expansion to be constructed which yields the correct coefficient of
Consider the following semilinear
The coefficient of the first and second order derivatives have the same sign, so the boundary layer will occur at the left boundary
and the solution is
The one-term inner expansion
which gives
Next, consider the quasilinear problem
The nonlinearity is associated with the first derivative term. The location of the boundary layer depends on the relative sign of the first and second derivative coefficients. If we assume that the dependent variable is nonnegative throughout the interval
with the solution
Assuming that the boundary layer thickness is
Its solution is
Application of perturbation techniques to partial differential equations, and other types of problems can be seen in the books [5, 6].
The authors declare no conflict of interest.
I owe a great debt to my mentor Prof. S Natesan, Department of Mathematics, IIT Guwahati, who introduced me to this topic. The chapter was discussed during my stay at IIT Guwahati.
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\\n\\nIntechOpen has the right to include/use the Author and Co-Authors names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Work and has the right to contact the Author and Co-Authors until the Work is publicly available on any platform owned and/or operated by IntechOpen.
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\\n\\nThird Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\\n\\nEntire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by, or on behalf of, the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (known as the "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of any fraudulent pre-contract misrepresentation or concealment.
\\n\\nWaiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
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\\n\\nNo partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Author or any Co-Author, nor authorize any party to make or enter into any commitments for, or on behalf of, any other party.
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\n\nCORRESPONDING AUTHOR'S GRANT OF RIGHTS
\n\nSubject to the following Article, the Author grants to IntechOpen, during the full term of copyright, and any extensions or renewals of that term, the following:
\n\nThe foregoing licenses shall survive the expiry or termination of this Publication Agreement for any reason.
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\n\nSubject to the license granted above, copyright in the Work and all versions of it created during IntechOpen's editing process, including all published versions, is retained by the Author and any Co-Authors.
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\n\nThe Author shall respect confidentiality during and after the termination of this Agreement. The information contained in all correspondence and documents as part of the publishing activity between IntechOpen and the Author and Co-Authors are confidential and are intended only for the recipients. The contents of any communication may not be disclosed publicly and are not intended for unauthorized use or distribution. Any use, disclosure, copying, or distribution is prohibited and may be unlawful.
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\n\nThe Author and Co-Authors also confirm and warrant that: (i) he/she has the power to enter into this Publication Agreement on his or her own behalf and on behalf of each Co-Author; and (ii) has the necessary rights and/or title in and to the Work to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licences in this Publication Agreement. If the Work was prepared jointly by the Author and Co-Authors, the Author confirms that: (i) all Co-Authors agree to the submission, license and publication of the Work on the terms of this Publication Agreement; and (ii) the Author has the authority to enter into this biding Publication Agreement on behalf of each Co-Author. The Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each Co-Author.
\n\nThe Author agrees to indemnify IntechOpen harmless against all liabilities, costs, expenses, damages and losses, as well as all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of, or in connection with, any breach of the agreed confirmations and warranties. This indemnity shall not apply in a situation in which a claim results from IntechOpen's negligence or willful misconduct.
\n\nNothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\n\nTERMINATION
\n\nIntechOpen has the right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Author and/or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Author and/or any Co-Author (being a private individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Author and/or any Co-Author (as a corporate entity) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for, or enters into, any compromise or arrangement with any of its creditors.
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\n\nIntechOpen agrees to offer free online access to readers and use reasonable efforts to promote the Publication to relevant audiences.
\n\nIntechOpen is granted the authority to enforce the rights from this Publication Agreement on behalf of the Author and Co-Authors against third parties, for example in cases of plagiarism or copyright infringements. In respect of any such infringement or suspected infringement of the copyright in the Work, IntechOpen shall have absolute discretion in addressing any such infringement that is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
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\n\nWaiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
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\n\nNo partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Author or any Co-Author, nor authorize any party to make or enter into any commitments for, or on behalf of, any other party.
\n\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
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