- Research Article
- Open Access
A Simulation Study: The Impact of Random and Realistic Mobility Models on the Performance of Bypass-AODV in Ad Hoc Wireless Networks
© Ahed Alshanyour and Uthman Baroudi. 2010
- Received: 13 October 2009
- Accepted: 6 August 2010
- Published: 10 August 2010
To bring VANET into reality, it is crucial to devise routing protocols that can exploit the inherited characteristics of VANET environment to enhance the performance of the running applications. Previous studies have shown that a certain routing protocol behaves differently under different presumed mobility patterns. Bypass-AODV is a new optimization of the AODV routing protocol for mobile ad-hoc networks. It is proposed as a local recovery mechanism to enhance the performance of the AODV routing protocol. It shows outstanding performance under the Random Waypoint mobility model compared with AODV. However, Random Waypoint is a simple model that may be applicable to some scenarios but it is not sufficient to capture some important mobility characteristics of scenarios where VANETs are deployed. In this paper, we will investigate the performance of Bypass-AODV under a wide range of mobility models including other random mobility models, group mobility models, and vehicular mobility models. Simulation results show an interesting feature that is the insensitivity of Bypass-AODV to the selected random mobility model, and it has a clear performance improvement compared to AODV. For group mobility model, both protocols show a comparable performance, but for vehicular mobility models, Bypass-AODV suffers from performance degradation in high-speed conditions.
- Mobile Node
- Mobility Model
- VANET Application
- Group Mobility Model
- Reference Point Group Mobility Model
Research has gained a significant advance in the development of routing protocols for wireless ad hoc networks [1, 2]. The movement pattern of mobile nodes plays an important role in the performance analysis of mobile and wireless networks. Additionally, mobility has a major effect on the route stability and availability. For example, to maintain communication, signaling traffic is needed for route construction and subsequent route maintenance. The extra signaling traffic over the air interface consumes radio resources, and it increases the interferences that affect the performance of other mobile nodes. Therefore, movement modeling is an essential building block in analytical and simulation-based studies of such systems. Moreover, some researchers [3, 4] have observed that the performance of routing algorithms may be influenced by the choice of mobility models. For example, random models are not a good choice to simulate the real-world mobility scenarios because usually mobile users either move toward certain attraction points such as classrooms or train stations, or move in certain directions such as vehicles. Some attempts have been made to implement specific mobility scenarios that are more realistic [5–7]. However, implementing a generic and a realistic mobility model is challenging because the mobility requirement in MANET changes due to the application environments. Indeed, devising a realistic mobility model that accurately reflects actual user mobility is a key challenge in evaluating the performance of any routing algorithm, and it has a significant effect on the obtained results. If the model is unrealistic, invalid conclusions may be drawn.
The Ad hoc On-demand Distance Vector (AODV)  is a distributed reactive routing protocol. It reacts relatively fast to the topological changes, and it saves storage space as well as energy. AODV performs better than other reactive protocols  in more stressful situations, such as a large number of nodes and highly mobile environments, but it suffers from high routing overhead compared to the Dynamic Source Routing (DSR) protocol. Bypass-AODV  is one of the recently developed routing protocols. It is an optimization of the AODV for mobile ad hoc networks, which uses a specific strategy, cross-layer MAC-notification, to identify mobility-related packet loss, and then it sets up a bypass between the node at which the route failure occurred and its old successor via an alternative node. By restricting the bypass to a very small topological radius, route adaptations occur only locally and communication costs are small. This approach has two main properties: simplicity and very promising performance compared to other existing approaches.
To study the impact of other well-known random mobility models, Random Walk (RW)  and Random Direction Mobility (RDM) , on the performance of the Bypass-AODV routing protocol. In these two models, users move individually in random directions with random velocities.
To evaluate the performance of the proposed protocol with real-life applications by using one of the group mobility models, Reference Point Group Mobility (RPGM) , and two vehicular mobility models: Freeway (FRW) and Manhattan (MAN) . For RPGM, users move in groups toward certain attraction points, while for FRW and MAN they move like groups in certain directions with controlled velocities.
To evaluate mobility impacts, we opt to simulation methodology for the following reasons. First, carrying out real experimental verification on the same scale as we carried out our simulation in is very difficult. Second, the theoretical analysis is not tractable for these networks with such complex mobility settings. The simulation results show that the Bypass-AODV routing protocol is insensitive to the random mobility pattern used in simulation. Under group mobility models, Bypass-AODV and AODV have similar performance. Although Bypass-AODV is a suitable choice for VANET applications at low to moderate speeds, it shows performance degradation at high speeds due to the unnecessary increase in the route length.
Our findings in this paper shall help the research community in understanding better the behavior of the studied protocols and their implications on new applications such as VANET networks. Moreover, this paper provides future directions for new studies in this interesting area.
The remainder of this paper is organized as follows. In Section 2, we briefly present the AODV routing protocol, and then we present our enhanced local recovery routing scheme, Bypass-AODV, and we outline its advantages. Section 3 describes commonly used mobility models and their applications. Section 4 presents the network simulator (nss')  simulation environment used to evaluate the performance of routing protocols under the selected mobility models. Section 5 discusses the performance of Bypass-AODV and original AODV. Finally, Section 6 summarizes the paper and suggests future research directions.
In this section, we shall summarize the basics of AODV and Bypass-AODV routing protocols.
2.1. AODV Routing Protocol
AODV is a reactive routing protocol used for dynamic wireless networks where nodes might enter and leave the network frequently. It is an on-demand routing algorithm that builds routes when desired by source nodes. When a source node desires a route to a destination for which it does not already have a route, it broadcasts a route request message (RREQ) to its immediate neighbors. If any of its neighbors has a valid route to the destination, it replies with a route reply message (RREP). Otherwise, nodes, neighbors rebroadcast the RREQ. This process of broadcasting continues until the RREQ reaches the requested destination or reaches a node with a fresh enough route to that destination. As a result, several RREPs may be sent back to the source node, which in turn chooses the suitable route. To ensure loop-free and route-freshness properties, a combination of sequence numbers and hop counts is associated with the RREQ. Sequence numbers and hop counts are used by intermediate nodes to decide either to rebroadcast the RREQ or to discard it.
AODV has a local maintenance scheme to maintain the routes as long as they are active. When a link break in an active route occurs, the node upstream of that break tries to repair the route if it is closer to the destination than the source node. To repair the link break, the node broadcasts an RREQ for that destination. Otherwise, the node makes a list of unreachable destinations consisting of the unreachable neighbor and any additional destinations in its local routing table that use the unreachable neighbor as the next hop. Then, the node broadcasts a route error message (RERR) to notify its neighbors to invalidate the routes using the broken link.
2.2. Bypass-AODV Routing Protocol
Bypass-AODV uses cross-layer MAC notification to identify mobility-related packet loss, and then it triggers the routing layer to start a local repair process. It allows the upstream node of the broken link to set up a bypass to connect with the downstream node via an alternative node. The MAC-notification message is used to distinguish between mobility-related packet loss and other source-related packet losses (signal interference, packet error rate, fading environment, and packet collision). Unlike AODV, the bypassing mechanism minimizes routing overheads by limiting the area of route bypass search based on spatial locality where a node cannot move too far too soon. Thus, with high probability, the new distance between the broken links end nodes will not exceed 2 hops. Moreover, bypass-AODV minimizes packet losses because it has the ability to repair the broken link regardless of its location.
However, packet losses occur when route bypassing does not work, specifically when the distance between broken links end nodes is 2 hops. In such a case, Bypass-AODV follows AODV link invalidation scheme. Several bypasses for the same route may lead to an unnecessary increase in the route hop count. To handle this issue, the bypassed-route is a temporary route that lasts for a period long enough to transmit packets that left the source node.
Mobility models can be categorized into two categories: entity and group mobility models. The entity mobility models represent the behavior of an individual node or group of nodes independently from other nodes. On the other hand, the group mobility models take into account the interaction among individual mobile nodes. Group mobility models are more suitable for some ad hoc network scenarios such as groups of soldiers in military actions or a group of fire fighters in action. In this section, in addition to RWP model, we will discuss two other random mobility models: RW and RDM. Next, we discuss the RPGM, FRW and MAN mobility models.
3.1. Random Walk Mobility Model (RW)
This model was originally proposed to emulate the unpredictable movement of particles in physics. In this model, a node moves from its current position to a new position by selecting a random direction and a random speed. The node randomly and uniformly selects its new direction from and speed from . During the time interval t, the node moves with the velocity vector . As the node reaches the boundary of the simulation region, it bounces back to the simulation region with an angle of or . The Random Walk model is memoryless it generates an unrealistic movement pattern, and hence it does not match real-life applications.
3.2. Random Waypoint Mobility Model (RWP)
RWP is widely accepted, mainly due to its simplicity of implementation and analysis. However, RWP fails to capture the characteristics of temporal dependency (i.e., the velocities at two different time slots are dependent) spatial dependency (i.e., the movement pattern of mobile nodes may be influenced by and correlated with nodes in its neighborhood), and geographic constraints (nodes' movements are restricted by obstacle, along streets and freeways) .
3.3. Random Direction Mobility Model (RDM)
The spatial node distribution of RWP is transformed from uniform node distribution to nonuniform distribution as the simulation time elapses and finally it reaches a steady state. In steady state, the mobile nodes are concentrated at the central region and are almost zero around the boundaries [12, 13]. The RDM model  was proposed to overcome such phenomenon. In RDM, the node randomly and uniformly chooses a direction and moves along that direction until it reaches a boundary. After reaching the boundary and stopping for some , it randomly and uniformly chooses another direction to travel. Therefore, the resultant node distribution from this model is more stable than that of RWP.
3.4. Reference Point Group Mobility Model (RPGM)
3.5. Freeway Mobility Model (FRW)
3.6. Manhattan Mobility Model (MAN)
We implement a simulation model using the ns to evaluate the performance of Bypass-AODV. Free Space propagation model is used to predict the signal power strength at the receiver side. The signal strength is used to determine if the frame is received successfully. ns mainly uses three thresholds to determine whether a frame is received correctly by the receiver. If the signal strength of the frame is less than the carrier sensing threshold (CSThresh), the frame is discarded in the PHY module and will not be visible to the MAC layer. If the signal strength of the received frame is stronger than the reception threshold (RxThresh), the frame is received correctly. Otherwise, the frame is tagged as corrupted and the MAC layer will discard it. When multiframes are received simultaneously by one mobile node, it calculates the ratio of the strongest frame's signal strength to the sum of other frames' signal strengths. If it is larger than the capturing threshold (CPThresh), the frame will be received correctly and other frames are ignored. Otherwise, all frames are collided and discarded. In our simulation, we choose TCP instead of UDP to evaluate the performance of our proposed protocol against large data packets and excessive overhead. The IEEE 802.11 MAC standard  and the TCP New-Reno are used at the MAC and TCP layers, respectively. The transmission rate is assumed to be constant at 1 Mbps.
The routing overhead ratio is the ratio of the amount in bytes of control packets transmitted to the amount in bytes of data packets received. This measure is important to estimate the cost of introducing the new protocol.
The goodput of the TCP is the number of sequenced bits that a TCP receiver receives per unit of time. This measure will show the effectiveness of the routing protocol from the application perspective.
The "goodput improvement ratio" is the TCP goodput observed with a Bypass-AODV strategy as compared to the standard AODV routing strategy.
Transmission range ( )
Transmission bit rate
1000 m × 1000 m
Number of nodes
Number of TCP connections
Maximum speed ( )
1, 5, 10, 20, 30, and 40 m/sec
Pause time ( )
SDR and ADR
In this section, we examine the impact of different random mobility models as well as group and vehicular mobility models on the performance of Bypass-AODV and AODV routing protocols.
5.1. Impact of Node Speeds on TCP Connection Length
Let us first present the statistical results for the impact of node speeds on the connection hop counts for RPGM, FRW, and MAN mobility models. These findings are important for understanding the behavior of routing protocols and their effect on TCP performance.
5.2. Impact of Random Mobility Models on Bypass-AODV
The RWP, RW, and RDM models are used to evaluate the performance of Bypass-AODV and AODV. Our objective is to study the performance of Bypass-AODV on both long and short TCP connections (in terms of hop counts) To achieve this objective, we make the TCP connection's end nodes static, while other nodes are allowed to move in accordance with the assumed mobility model with a maximum speed of 20 m/s. Hence, the physical distance (the physical distance between the source and the destination of a TCP connection remains relatively unchanged during a simulation run. It is worth to note that the minimum distance between TCP connection end nodes in terms of the number of hops, assuming nodes use their maximum transmission range (180 m)) between the connection's end nodes remains relatively unchanged during a simulation run.
Actually, all the nodes in the ad hoc network share the same transmission medium. If a node is transmitting, other nodes within a certain range of the transmitting node cannot transmit. Two ranges are defined by the IEEE 802.11 MAC and are used in our simulation: the transmission range and the sensing range. The transmission range is the maximum distance between two nodes, such that a signal transmitted by one node can be received by the other node and can be decoded correctly. The sensing range is defined as the maximum distance between two nodes, such that a signal transmitted by one node can be received by the other node, but cannot be decoded correctly. The sensing range is much larger than the transmission range. In our simulation setting, the transmission range is 180 m while the sensing range is 400 m. The IEEE 802.11 MAC protocol ensures that while a node is transmitting, other nodes within its sensing range cannot transmit.
For a number of hops , the connection end nodes start to reside at boundaries, and therefore Bypass-AODV shows clear enhancement in performance with RW and RDM models due to the uniform distribution of nodes that creates homogeneous and highly connected networks. However, the nonuniform distribution of mobile nodes may partition the network frequently as in RWP. Finally, these findings confirm previous results in the literature, namely, a routing protocol may behave differently under different mobility models especially for long connections .
5.3. Impact of Group Mobility Models on Bypass-AODV
we explore the dependency of routing protocols performance on the movement pattern used in the simulated environment. For the RPGM model, we use four groups of 15 nodes, each one is moving independently of the others and in an overlapping fashion.
The connection hop count distribution (hc); node's speed is 20 m/sec.
Furthermore, Figure 11 illustrates that the RPGM movement pattern doubles the goodput of both routing protocols relative to RWP. This considerable enhancement in goodput is due to the spatial dependency nature of the RPGM model, which increases the lifetime of the routes.
5.4. Impact of Vehicular Mobility Models on Bypass-AODV
Accurate evaluation of mobility impact on the routing protocols requires the testing of different mobility patterns. Otherwise, the observations made and the conclusions drawn from the simulation studies may be misleading. In this paper, we investigated the behavior of an optimized Bypass-AODV for a wide range of mobility models including VANET models. Simulation results show that Bypass-AODV is insensitive to random mobility models and has a clear performance improvement compared to AODV. Moreover, Bypass-AODV always outperforms AODV when nodes are uniformly distributed for the long TCP connections. In addition, Bypass-AODV has a comparable performance under group mobility model compared to AODV. Currently, Bypass-AODV is not suitable for handling VANET applications at very high speeds. As a future work, Bypass-AODV needs more improvement in order to handle VANET applications. We believe that several parameters, such as vehicle speed and direction, are necessary for appropriate route selection in VANET applications. The route selection process should be responsive and intelligent to avoid unnecessary long paths and at the same time to make use of neighboring nodes to receive the requested service. In fact, several studies have shown that proactive routing protocols are unreliable for VANET applications [17, 18].
This paper is supported by King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia under Fast Track project FT 2005-16.
- Perkins CE, Royer EM: The ad hoc on-demand distance vector protocol. In Ad Hoc Networking. Addison-Wesley, Reading, Mass, USA; 2001:173-219.Google Scholar
- Johnson DB, Maltz DA, Broch J: DSR: the dynamic source routing protocol for multi-hop wireless ad hoc networks. In Ad Hoc Networking. Addison-Wesley, Reading, Mass, USA; 2001:139-172.Google Scholar
- Camp T, Boleng J, Davies V: A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing 2002, 2(5):483-502. 10.1002/wcm.72View ArticleGoogle Scholar
- Madsen TK, Fitzek FHP, Prasad R: Impact of different mobility models on connectivity probability of a wireless ad hoc network. Proceedings of the International Workshop on Wireless Ad-Hoc Networks, June 2004 120-124.Google Scholar
- Bai F, Sadagopan N, Helmy A: Important: a framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks. Proceedings of the 22nd Annual Joint Conference on the IEEE Computer and Communications Societies (INFOCOM '03), April 2003 825-835.Google Scholar
- Choffnes DR, Bustamante FE: An integrated mobility and traffic model for vehicular wireless networks. Proceedings of the 2nd ACM International Workshop on Vehicular Ad Hoc Networks (VANET '05), September 2005 69-78.View ArticleGoogle Scholar
- Baumann R, Heimlicher S, May M: Towards realistic mobility models for vehicular ad-hoc networks. Proceedings of the Mobile Networking for Vehicular Environments (MOVE '07), May 2007 73-78.Google Scholar
- Park VD, Corson MS: A highly adaptive distributed routing algorithm for mobile wireless networks. Proceedings of the 16th IEEE Annual Conference on Computer Communications (INFOCOM '97), April 1997 1405-1413.Google Scholar
- Alshanyour A, Baroudi U: Bypass-AODV: improving performance of ad hoc on-demand distance vector (AODV) routing protocol in wireless ad hoc networks. Proceedings of the International Conference on Ambient Media and Systems (Ambi-sys 2'08), 2008Google Scholar
- Hong X, Gerla M, Pei G, Chiang C-C: A group mobility model for ad hoc wireless networks. Proceedings of the 2nd ACM International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM '99), 1999Google Scholar
- Fall K, Varadham K http://www.isi.edu/nsnam/ns/ns-documentation.html
- Bettstetter C, Wagner C: The spatial node distribution of the random waypoint mobility model. Proceedings of the 1st German Workshop on Mobile Ad Hoc Networks (WMAN '02), 2002 41-58.Google Scholar
- Blough DM, Resta G, Santi P: A statistical analysis of the long-run node spatial distribution in mobile ad hoc networks. Proceedings of the ACM International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM '02), September 2002 30-37.Google Scholar
- Royer EM, Melliar-Smith PM, Moser LE: An analysis of the optimum node density for ad hoc mobile networks. Proceedings of the International Conference on Communications (ICC '01), June 2000 857-861.Google Scholar
- Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE standard 802.11, 1997Google Scholar
- Jayakumar G, Ganapathi G: Reference point group mobility and random way-point models in performance evaluation of MANET routing protocols. Journal of Computer Systems, Networks, and Communications 2008, 2008:-10.Google Scholar
- Nzouonta J, Rajgure N, Wang G, Borcea C: VANET routing on city roads using real-time vehicular traffic information. IEEE Transactions on Vehicular Technology 2009, 58(7):3609-3626.View ArticleGoogle Scholar
- Prasanth K, Duraiswamy K, Jayasudha K, Chandrasekar C: Improved packet forwarding approach in Vehicular ad hoc networks using RDGR algorithm. IJNGN 2010., 2(1):Google Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.