Routing Protocols And Mobility Models Computer Science Essay

Published Date: 02 Nov 2017

CHAPTER 3

2.1 Routing Protocols

A routing protocol determines the way two communication entities exchange information, establish a route, make data forwarding decision, take action in maintaining the route and recovering from routing failure. In VANETs the routing protocols can be classified as shown in Fig. 3.1.

Figure 3.1: Classification of Routing Protocols in VANETs.

In VANETs routing protocols can be classified as Topology based and Geographic (position) based.

2.1 Topology-based routing protocols

Topology-based routing protocols use link’s information that exists in the network to perform packet forwarding. They can be further divided into proactive (table-driven) and reactive (on-demand) routing protocols.

Proactive protocols keep track of all destinations in the ad hoc network and have the advantage that communications with arbitrary destination experience minimal initial delay from the point of view of the application. DSDV is an example of Proactive Protocol. When the application starts, a route can be immediately selected from the route table. However, proactive protocols suffer the disadvantage of additional control traffic that is needed to continually update stale route entries.

Reactive protocols have been designed so that routing information is acquired only when it is actually needed. Reactive protocols may often use far less bandwidth for maintaining the route table at each node, but the latency for many applications will drastically increase [8]. AODV, DSR and AOMDV are examples of reactive routing protocols.

2.2 Geographic (position) based routing

In geographic (position-based) routing, the packet forwarding decision by a node is primarily made based on the position of a packet’s destination and the position of the node’s one-hop neighbors. The position of the destination is stored in the header of the packet by the source. The position of the node’s one-hop neighbors is obtained by the beacons sent periodically with random jitter (to prevent collision). Nodes that are within a node’s radio range will become neighbors of the node. Geographic routing assumes that each node knows its location, and the sending node knows the receiving node’s location by the increasing popularity of Global Position System (GPS) unit from an onboard Navigation System. The route is determined based on the geographic location of neighboring nodes as the packet is forwarded. There is no need of link state exchange nor route setup.

2.2.1 Greedy Perimeter Stateless Routing (GPSR)

(GPSR) (Karp, 2000), a node forwards a packet to an immediate neighbor which is geographically closer to the destination node. This mode of forwarding is termed greedy mode. When a packet reaches a local maximum, a recovery mode is used to forward a packet to a node that is closer to the destination than the node where the packet encountered the local maximum. The packet resumes forwarding in greedy mode when it reaches a node whose distance to the destination is closer than the node at the local maximum to the destination.

Out of the protocols shown in Fig.3.1, we have simulated the following protocols in our work.

2.1.1 Destination Sequence Distance Vector Routing Protocol (DSDV)

DSDV protocol transmits the packets between the nodes of the network using routing tables stored at each node. Each route table, at each of the nodes, lists all destinations and the number of hops to each. Each route table entry is tagged with a sequence that is originated by the destination node. To maintain the consistency of route tables in a dynamically varying topology, each node periodically transmits updates, doing so immediately when significant information is available. Routing information is advertised by broadcasting or multicasting the packets that are transmitted periodically and incrementally as topological changes are detected. Data is also kept about the length of time between the arrival of the first and the arrival of the best route for each particular destination. On the basis of the data, a decision may be made to delay advertising routes that are about to change, thus dampening fluctuations of the route tables. The advertisement of possibly unstable routes is delayed to reduce the number of rebroadcasts of possible route entries that normally arrive with the same sequence number.

Route Advertisement

The DSDV protocol requires each mobile node to advertise, to each of its current neighbors, its own route table. The entries in this list may change fairly dynamically over time, so the advertisement must be made often enough to ensure that every mobile computer can almost always locate every other mobile computer in the collection. In addition, each mobile computer agrees to relay data packets to other computers upon request. This agreement places a premium on the ability to determine the shortest number of hops for a route to a destination. In this way a mobile computer may exchange data with any other mobile computer in the group even if the target of the data is not within the range for direct communication.

2.1.2 Dynamic Source Routing Protocol (DSR)

DSR is an efficient routing protocol designed for use in multi hop wireless ad hoc networks of mobile nodes. In DSR protocol network nodes cooperate to forward packets for each other to allow communication over multiple "hops" between nodes not directly within wireless transmission range of one another. As nodes in the network move about or join or leave the network, and as wireless transmission conditions such as sources of interferences change, all routing is automatically maintained by DSR. The working of DSR protocol is classified in two important steps:-

Route Discovery- In this step a node wishing to send a packet to a destination node obtains a source route to it. Route discovery is used only when a node does not knows the route to it. When some node originates a new packet destined for some node, it places in the header of the packet a source route giving the sequence of hops that the packet should follow. The node obtains a suitable source route by searching its Route Cache of routes previously learned, but if no route is found in its cache it initiates the Route Discovery protocol to find a new route to destination dynamically. Fig 2.1.2 illustrates the route discovery process in which node A is attempting to discover a route to E.

When a node receives a ROUTE REQUEST, if it is the target of the Route Discovery it returns a ROUTE REPLY message to the Route Discovery initiator, giving a copy of the accumulated route record from the ROUTE REQUEST; when the initiator receives this ROUTE REPLY, it caches this route in its Route Cache for use in sending subsequent packets to the destination.

Figure 2.1.2.1: Route Discovery Example with Node A as the Initiator and Node E as the Target

Route Maintenance – When originating or forwarding a packet using a source route, each node transmitting the packet is responsible for confirming that the packet has been received by the next hop along the source route; the packet is retransmitted (up to a maximum number of attempts) until this confirmation of receipt is received. If the packet is retransmitted by some hop the maximum number of times and no receipt confirmation is received, this node returns a ROUTE ERROR message to the original sender of the packet, identifying the link over which the packet could not be forwarded.

Figure 2.1.2.2: Route Maintenance Example, Node C is unable to forward a packet from A to E over its link to next hop, D.

For example in Fig. 2.1.3 if C is not able to deliver the packet to the next hop D, C returns a ROUTE ERROR to A, stating that the link from C to D is currently "broken." Node A then removes this broken link from its cache, and any retransmission of the original packet is a function for upper-layer protocols such as TCP.

2.1.3 Ad hoc on Demand Distance Vector Routing Protocol (AODV)

The AODV protocol provides unicast, multicast and broadcast communication ability. AODV currently utilizes only symmetric links between neighboring nodes, but otherwise does not depend specifically on particular aspects of the physical medium across which packets are disseminated. AODV uses route tables to store pertinent routing information. The route table is used to store the destination and next-hop IP address as well as the destination sequence number. When a node wishes to send a packet to some destination node, it checks its route table to determine whether it has a current route to that node. If so, it forwards the packet to the appropriate next hop toward the destination. If the node does not have a valid route to the destination, it initiates a route discovery process. The source node broadcasts a route request packet (RREQ) and then sets a timer to wait for a reply. To respond to the RREQ, the node must have an unexpired entry for the destination in its route table. The sequence number associated with that destination must be at least as great as that indicated in the RREQ. This prevents the formation of routing loops by ensuring that the route returned is never old enough to point to a previous intermediate node. Otherwise, the previous node would have responded to the RREQ. If the node is able to satisfy these two requirements, it responds by unicasting a RREP back to the source. If it is unable to satisfy the RREQ, it increments the RREQ’s hop count and then broadcasts the packet to its neighbors [8].

2.1.4 Ad hoc on Demand Multipath Distance Vector Routing Protocol (AOMDV)

AOMDV extends the AODV protocol to discover multiple paths between the source and the destination in every route discovery. Multiple paths so computed are guaranteed to be loop-free and disjoint. In AOMDV, RREQ propagation from the source towards the destination establishes multiple reverse paths both at intermediate nodes as well as the destination [15]. Multiple RREPs traverse these reverse paths back to form multiple forward paths to the destination at the source and intermediate nodes. Each duplicate route advertisement received by a node defines an alternate path to the destination [S. R Biradar].These alternate paths are found to be useful in reducing route discovery frequency [15, 16].This mechanism reduces route discovery latency and the routing overheads.

To find node-disjoint routes using AOMDV, each node does not immediately reject duplicate RREQs. Each RREQs arriving through a different neighbor of the source defines a node-disjoint path. This is because nodes cannot broadcast duplicate RREQs. So any two RREQs arriving at an intermediate node through a different neighbor of the source could not have traversed the same node. In an attempt to get multiple link-disjoint routes, the destination replies to duplicate RREQs, the destination only replies to RREQs arriving through unique neighbors. After the first hop, the RREPs follow the reverse paths, which are node disjoint and thus link-disjoint. The trajectories of each RREP may intersect at an intermediate node, but each takes a different reverse path to the source to ensure linkdisjointness. The advantage of using AOMDV is that it allows intermediate nodes to reply to RREQs, while still selecting disjoint paths.

1.1 MOBILITY Models

The mobility of vehicles is one of the most important factors in the performance evaluation of VANETs. The performance of protocol is highly influenced by them. The understanding of each observed mobility pattern can help to improve the network behavior []. Mobility Models are classified into five types as follows:-

Individual Mobility Model.

Group Mobility Model.

Hybrid Mobility Model.

Intelligent Driver Model.

When dealing with vehicular mobility modeling, some authors have distinguished between macro-mobility and micro-mobility. For macro-mobility they refer to all the macroscopic aspects which influence vehicular traffic, for example: the road topology, constrained car movements, the per-road speed limits. Micro-mobility refers instead to the driver’s individual behavior when interacting with other drivers or with the road infrastructure, for instance, traveling speed under different traffic conditions, acceleration, deceleration and overtaking criteria. For a trustworthy VANET simulation that both macro-mobility and micro-mobility descriptions are jointly considered when modeling vehicular movements.

1.2 INDIVIDUAL MOBILITY MODEL

Individual models can be divided into memory less and smooth model [3] based on whether the nodes have the ability of memory. Memory less model means that node’s movement parameters such as speed, direction, etc, at any time t are independent from that at time t-Δt. Random waypoint (RWP) , Random direction model (RDM),Gauss Markov Model and Street Random Waypoint are widely used Individual Mobility Model. In this model, each node selects a random destination in the simulation area, and move to the destination with a random speed between Vmin and Vmax, and then repeats this process until the end of the simulation. In RWP, nodes select a new destination and speed after waiting a random time, which is the only difference between RWP and RWM. As an improvement of RWP, RDM is used to overcome the disadvantage in RWP that nodes tend to distribute in the center of the simulation area, and nodes are distributed more evenly in RDM. Smooth model means that nodes’ parameters at any time t are related to the parameters at time t-Δt, the speed and direction change smoothly in such model, and which are closer to the real behavior of mobile nodes. GM and Boundless Simulation Area model are such kind of model.

1.2.1 Random way point Model

Random Waypoint Model [12, 13] is the most widely used and studied mobility model. In this

model, a host randomly chooses a destination called waypoint and packet moves towards it in a

straight line with a constant velocity, which is selected randomly from some given range. After

it reaches the waypoint, it pauses for some time and then repeats the procedure. For implementation in NS-2 , at every instant, a node randomly chooses a destination and moves towards it with a velocity chosen randomly from [0,Vmax], where Vmax is the maximum allowable velocity for every mobile node. After reaching the destination, the node stops for a duration by the 'pause time' parameter. Then, it again chooses a random destination and repeats the whole process again until the simulation ends.

1.2.2 Gauss-Markov Model

The Gauss-Markov Mobility Model is designed to adapt to different levels of randomness via one tuning parameter. [1,10].Initially each mobile node (MN) is assigned a speed and direction. At fixed intervals of time n, movement occurs by updating the speed and direction of each MN. The value of speed and direction at the nth instance is calculated based upon the value of speed and direction at the (n-1)th instance and a random variable using equations:

sn = sn-1 + (1-)s + (1-2)sxn-1 (3)

dn = dn_1 + (1-)d +  (1-2)dxn-1 (4)

sn and dn are the new speed and direction of the MN at interval n.  is the tuning parameter used to vary the randomness, where 0 <=  <= 1. S and d are constants representing the mean value of speed and direction as n and sxn-1 and dxn-1 are random variables from a Gaussian

distribution. Totally random values (or Brownian motion) are obtained by setting  = 0 and linear motion is obtained by setting  = 1. Intermediate levels of randomness are obtained by varying it -between 0 and 1. At each time interval the next location is calculated based on the current location, speed, and direction of movement. Specifically, at time interval n, an MN’s positionis given by the equations:

xn = xn-1+sn-1 cosdn-1 (5)

yn = yn-1 + sn-1 sindn-1 (6)

Where (xn,yn) and (xn-1,yn-1) are the x and y coordinates of the MN’s position at nth and (n-1)th

time intervals respectively. To ensure that a MN does not remain near an edge of the grid for a

long period of time, the they are forced away from an edge when they move within a certain distance of the edge. This Model can eliminate the sudden stops and sharp turns encountered in the Random Walk Mobility Model by allowing past velocities to influence future velocities.

1.2.3 STRAW (Street Random Waypoint)

STRAW [20] is also a model using real maps of TIGER/Line [54]. Like the other models, except freeway, roads include one lane in each direction and it is divided into segments. The model is basically composed of three modules: intra-segment mobility manager, inter-segment mobility manager, and finally the rout management and execution module. At the beginning of the simulation the nodes are placed randomly one behind the other, they move using the car following and try to accelerate until reaching the maximum speed of the segment. The first module manages this movement until reaching an intersection. The security distance is maintained, but the overtaking is not allowed. At crossroads the vehicles always slow down, even when they change a segment and turn without a full stop, which is realistic. The second module defines the traffic control mechanism including both stop signals and traffic lights, which are put on crossroads according to the class of the intersected roads. In addition to this usual control form, the module makes sure that the next segment to take contains enough available space before moving the vehicle towards it. If it is fully busy, the vehicle waits at the crossroads (at the end of the first segment). The last module selects the routes to be taken by each vehicle during the simulation. In the first one the direction is randomly selected at each intersection, for example, when reaching an intersection, the vehicle randomly decides whether to continue straight forward or to turn and change the road. On the other hand in the second approach a destination is selected toward which the vehicle moves using the shortest path.

1.3 Group Mobility Model

Some mobility scenarios in MANETs have the characteristic of group moving [9]. For example, in tactical Internet environment, mobile nodes usually show the characteristic of group moving due to special mission. In traffic scenes, the vehicles in one lane may satisfy group moving characteristic.

1.3.1 Reference Point Group Mobility (RPMG) Model

Each group has a logical center (group leader) that determines the group's motion behavior. Initially, each member of the group is uniformly distributed in the neighborhood of the group leader. Subsequently, at each instant, every node has a speed and direction that is derived by randomly deviating from that of the group leader [13]. Each node deviates its velocity (both speed and direction) randomly from that of the leader. The group motion behavior is important in some applications like ubiquitous computing, military deployment etc. The movement can be characterized as follows:

|Vmember(t)| = |Vleader(t)| + random()*SDR*max_speed (1)

θmember(t) = θleader(t) + random() *ADR*max_angle (2)

Where 0 <= SDR, ADR <= 1. SDR is the Speed Deviation Ratio and ADR is the Angle Deviation Ratio. They are used to control the deviation of the velocity (magnitude and direction) of group members from that of the leader. Since the group leader mainly decides the mobility of group members, group mobility pattern is expected to have high spatial dependence for small values of SDR and ADR.

1.4 Hybrid Mobility Model

Hybrid mobility model shows the following characteristics: in model, some nodes satisfy individual moving characteristics and other nodes satisfy group moving characteristics, or one node sometimes satisfies individual moving characteristics and sometimes satisfies group moving characteristics. For example, when the number of the vehicles in one lane is large, the speed and direction of the vehicles is approximate and the scenario satisfies group mobile model characteristics. But when the number of the vehicles in one lane is small or the relative speed of mobile nodes is large, and this scenario satisfies individual mobile characteristics. The typical hybrid mobility models are Manhattan model, Freeway model and City Section Mobility (CSM) Model.

1.4.1 Manhattan Mobility Model

The Manhattan model emulates the movement pattern of mobile nodes on streets defined by maps [12,13]. It is useful in modeling movement in an urban area where a pervasive computing service between portable devices is provided. Maps are composed of a number of horizontal and vertical streets used in this model. Each street has two lanes for each direction (North / South direction for vertical streets, East / West for horizontal streets). The mobile node is allowed to move along the grid of horizontal and vertical streets on the map. At an intersection of a horizontal and a vertical street, the mobile node can turn left, right or go straight. This choice is probabilistic, the probability of moving on the same street is 0.5, the probability of turning left is 0.25 and the probability of turning right is 0.25. The velocity of a mobile node at a time slot is dependent on its velocity at the previous time slot. Also, a node’s velocity is restricted by the velocity of the node preceding it on the same lane of the street.

1.4.2 Freeway model

Freeway [7] is a generated-map-based model, defined in the simulation area, represented by a generated map, includes many freeways, each side of which is composed of many lanes. No urban roads, thus no intersections are considered in this model. At the beginning of the simulation the nodes are randomly placed on the lanes, and they move using history-based speeds, where the speed of each vehicle smoothly changes following a random acceleration. In addition to the realism related to the acceleration and the history-based speed, the model defines a security distance that should be maintained between two subsequent vehicles in a lane. If the distance between two vehicles is less than this required distance, the second one decelerates to enable the forward vehicle moving away. The change of lanes is not allowed in this model. The vehicle moves on the lane it is placed in until reaching the simulation area limit, and then it is placed again randomly in another position and repeats the process.

City Section Mobility (CSM) Model

CSM [27] can be viewed as a hybrid model between Random Waypoint Model (RWP), in which mobile nodes move randomly and freely without restrictions, and Manhattan as it introduces the principle of RWP, especially the pause-time and random selection destination, within a generated-map-based urban area. At each step of the vehicle's movement a random point is selected from the generated road map, towards which it moves following the shortest path. After reaching that destination, it remains there for a pause-time, and then repeats the process. The speed of nodes is constrained by the security distance, along with the maximum speed limit of the road.

1.5 INTELLIGENT DRIVER MODEL

The Intelligent driver model (IDM) [Martin Treiber, Ansgar Hennecke, and Dirk Helbing. Congested Traffic States in Empirical Observations and Microscopic Simulations. PHYSICAL

REVIEW E, 62:1805, 2000.] is a microscopic, time-continuous car-following model for the simulation of freeway and urban traffic. It is one of the most popular traffic mobility models. IDM models the behavior of a driver by means of a set of rules developed in order to avoid any collision with leading vehicle. IDM takes into account two aspects of a driver: the tendency to accelerate in order to reach a desired speed and the tendency to decelerate due to the interaction with leading vehicle.

The acceleration assumed in the IDM is a continuous function of the velocity, the gap, and the velocity difference (approaching rate) to the leading vehicle:

This expression is an interpolation of the tendency to accelerate with on a free road and the tendency to brake with deceleration when vehicle comes too close to the vehicle in front. The deceleration term depends on the ratio between the "desired minimum gap" and the actual gap , where the desired gap

is dynamically varying with the velocity and the approaching rate [].

Stop Sign Model (SSM)

Contrary to the previous models, SSM [54] integrates a traffic control mechanism. In every crossroads, a stop signal is set, which obliges vehicles to slow down and make a pause there. This model is based on real maps of the TIGER/Lines database [5], but all roads are assigned a single lane in each direction. A vehicle should never overtake its successor (like in all the models presented before) and should tune its speed to keep the security distance. If many vehicles arrive at an intersection at the same time, they make a queue, and each one waits for its successor to traverse the crossroads. This results in gathering of nodes, and hugely affects the network connectivity as well as the mobility (average speeds). According to the authors, the problem with this model is the unrealistic disposition of the spot signals, since it is impossible to find a region with spot signals at each intersection, therefore, they improved SSM and they proposed TSM.

Traffic Sign Model (TSM)

In TSM model [54], stop signals are replaced by traffic lights. A vehicle stops at crossroads if it encounters a red stoplight; otherwise it continues its movement. When the first vehicle reaches the intersection, the light is randomly turned red with probability p (thus turned green with probability 1-p). If it turns red, then it remains so for a random delay (pause-time) forcing the vehicle to stop, as well as the ones behind it. After the delay, it turns red, and then the nodes traverse the crossroads one after the other until the queue is empty. When the next vehicle arrives at the crossroads the process is repeated.