- Email: [email protected]
Virtual reality mobility model for wireless ad hoc networks
Journal of Systems Engineering and Electronics Vol. 19, No. 4, 2008, pp.819–826
Virtual reality mobility model for wireless ad hoc networks Yu Ziyue1 , Gong Bo1,2 & He Xingui3 1. School of Computer Science & Engineering, Beijing Univ. of Aeronautics and Astronautics, Beijing 100083, P. R. China; 2. Inst. of Command and Technology of Equipment, Beijing 101416, P. R. China; 3. School of Electronics Engineering and Computer Science, Peking Univ., Beijing 100871, P. R. China (Received May 10, 2007)
Abstract: For wireless ad hoc networks simulation, node’s mobility pattern and traffic pattern are two key elements. A new simulation model is presented based on the virtual reality collision detection algorithm in obstacle environment, and the model uses the path planning method to avoid obstacles and to compute the node’s moving path. Obstacles also affect node’s signal propagation. Considering these factors, this study implements the mobility model for wireless ad hoc networks. performance of protocols.
Simulation results show that the model has a significant impact on the
Keywords: wireless ad hoc networks, computer simulation, network protocols, mobility models.
1. Introduction Wireless ad hoc networks originate from military research and development, and recently have been used in several fields. Simulation plays an important role in the research of wireless ad hoc networks. In order to carry out an exhaustive research on the protocols of wireless ad hoc networks, all primary factors that affect the protocol performance must be considered. Fortunately, these primary factors have been simplified reasonably, and fall into two categories: the mobility model and the traffic model[1] . Since some wireless ad hoc network’s simulation models are simplistic, researchers find that there are several differences between the theoretical researches and the experimental results. Therefore, researchers proceed to consider other experimental aspects[2] . Currently, the random waypoint model (RWP) is one of the most popular mobility model for studying wireless ad hoc network protocol[2], and the latest important advance of this field is the obstacle mobility model (OMM)[3] . This article presents a mobility model based on virtual reality (VRMM, virtual reality mobility model). Firstly, the model synthesizes the random waypoint
model and the obstacle mobility model, and then introduces the virtual reality collision detection method to build a new mobility model. The mobility model can be used to solve two important issues. Firstly, this model uses the virtual reality collision detection method to process the position and time of the collisions; Secondly, based on random waypoint model and the obstacle mobility model, this model incorporates a collision detection methodology to process the interaction between nodes and obstacles. When a node meets an obstacle, the node plans its pathway using the artificial intelligent method. This model makes the movement of nodes more smooth and realistic than that done by other random models.
2. Research background There are several mobility models, which are used to analyze and simulate protocols of wireless ad hoc networks. This article uses the term “node” to denote any kind of network-enable device, for example, a user, a moving mobile terminal, or a device inside vehicle. Bettstetter[4] provides a better categorization and map of the mobile model, while a survey and comprehensive comparison of a variety of mobile models can
820 be found in Camp, et al [5] . A new special description of wireless ad hoc networks mobility model is given by Jardosh, et al[6] . Many of the analytical mobility models originate from some rather simple presumption, while models for simulation-based studies describe the movement of nodes in a slightly complicated manner. The remainder of this section focuses on several primary mobility models for wireless ad hoc networks. There exist several mobility models based on Guerin’s model[7] , which is commonly denoted as the random direction model. Several variations of the random direction model are proposed. Royer, et al[8] defined a simplified version of the random direction model, and the main difference is that every node moves until it reaches the boundary of the simulation terrain rather than moving only for some period of time. Hass, et al[9] put forward another version of the random direction model. Hong, et al also provided a group mobility model that has evolved from the random direction model.
Yu Ziyue, Gong Bo & He Xingui bility model. Before Jardosh and Belding-Royer presented the OMM, one common characteristic of all mobility models was that mobile nodes move in open, unobstructed terrain. OMM incorporates obstacles on the simulation terrain, and from this viewpoint, it is a more realistic model than the other earlier models. 3.1
VRMM framework
Commonly, the mobility model includes two functional blocks: motion constraints and traffic generator. When focusing on a macroscopic point of view, the virtual reality mobility model includes the following components: speed control, direction control, collision detection, path planning, and wireless signal transport control. A concept map for virtual reality mobility models can be depicted in Fig. 1. The solid part represents motion constraints, and the dashed part represents traffic generator.
Johnson, et al presented a random waypoint model, which was used in NS-2 network simulation tool, and has been one of the most widely used mobility model. Various performance evaluations of the mobile ad hoc network are based on this mobility model. The latest important advance of the mobility model for studying ad hoc network protocols is the obstacle mobility model presented by Jardosh, et al. This model can be stated as follows: Incorporation of obstacles to model the location of buildings within an environment, i.e., a college campus. Once the buildings are placed, this model uses the Voronoi diagram of obstacle vertices to construct movement paths; then nodes are randomly distributed across the paths and destinations are selected from the set of obstacles, and shortest path route computations are used to determine the path that each node will use to reach its selected destination. There are two important factors in OMM: the obstacles are all static, and in the beginning of the simulation, the paths for nodes movement are planned.
3. Mobility model and algorithm This section presents our model: virtual reality mo-
Fig. 1
VRMM framework
In the article, the virtual reality mobility model is divided into three parts to depict the following: (1) Incorporation of objects that can model static obstacles as well as dynamic obstacles. After there are obstacles in the simulation terrain, the mobility model uses the virtual reality collision detection method to find obstacles; (2) Use of artificial intelligent heuristic path search algorithm to plan a new pathway when any node finds obstacle in its pathway;
Virtual reality mobility model for wireless ad hoc networks (3) Determination of signal propagation owing to obstacles. 3.2
Speed and direction control
Modeling the speed behavior of nodes is based on the use of target speeds. A node moves with constant speed v until it arrives at a new target. Collision detection is an important research topic in several fields: computer simulation, virtual reality, computer animation, computer geometry, robotology, CAD/CAM[10] . There are several obstacles in the simulation terrain. When nodes move in this area, the nodes often meet obstacles. For the sake of keeping simulations realistic, it is necessary to detect the meet between nodes and obstacles, and then compute the response of nodes (collision detection). Thus, there are two problems: Firstly, to detect when and where the collision will happen; and secondly, to compute the node’s response after collision. Collision detection precision and real time are two important constraints. As for the real time of collision detection, the collision detection speed must be greater than the network simulation requirement, i.e., collision detection must be completed in network simulation event interval.
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There are several collision detection methods and algorithm libraries developed by researchers; however, many of these have pros and cons, and different usage. We can perform application requirement analysis based on three factors: model category, detection category, and scenario classification. As for the model category, since the simulation terrain can be considered as a planar and obstacles are disseminated in a planar, the planar model is satisfied. As for the detection category, it can be classified into precision and approximate collision detection. Thus, the model uses approximate collision detection. At last, since our model includes static and dynamic obstacles, the model uses dynamic scenario. The number of mobile nodes is set as m: N1 , N2 , N3 , . . ., Nm , and the number of obstacles is set as n: O1 , O2 , O3 , . . ., On . Tprev is the global time variable, and the model’s collision detection algorithm can be given as Fig. 3[10] . CheckForCollision (Tcurr, {N1 , . . ., Nm }, { O1 , . . ., On }) { //for each moving wireless node for(each Ni in { N1 , . . ., Nm }) do { //for each time interval for(t=Tprev to Tcurr, set step to dt) do {
Collision detection precision is determined by the application field. For networks simulation application, it is necessary to make approximate collision detection. When two objects not to collide each other, the model assumes that there is a collision between two objects; If there is only a little distance between two objects, and the distance is smaller than threshold value. An illustration of collision detection is given in Fig. 2.
//compute node position according time and //node velocity move node Ni to position according to time t; for(each Oj in { O1 , . . ., On }) do { //test node collides with obstacle if (Ni collides with Oj ) then reports that there is a collision at time t; } } Tprev = Tcurr; } } Fig. 3
Fig. 2
Moving nodes collide with obstacles
Collision detection algorithm
The model makes some improvements on the foundational collision detection algorithm: (1) Use of adaptive time step. The time loop step can be computed according to node speed and collision
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detection precision. The method is given as follows dt = CD P REC/speed
(1)
The result of algorithm A* is to obtain a pathway from start node to goal node without obstacle. 3.4
where speed denotes node’s speed, and CD PREC denotes collision detection precision. (2) Improvement of the approach for checking objects intersection. This can be done using application field, and tricks and tips[11] . (3) Integration of time loops. Since collision detection and network simulation both have time loops, the model must integrate the two time loops. 3.3 Pathway planning after collision detection There are two main purposes to make pathway planning: Firstly, to avoid obstacles; secondly, to search for the optimization pathway, which is often the shortest pathway[12]. Of course, there are various pathway planning methods. Some are traditional methods including steepest descent and optimum control, and it is difficult to use traditional methods in our model. Others are intellectual methods including dynamic programming, heuristic search, neural networks, simulated annealing and genetic algorithm etc. Dynamic programming and heuristic search are the popular methods in practice, but heuristic search is simple in theory, and the computing cost is smaller. We select algorithm A* of heuristic search to use in our model’s pathway planning. Algorithm A* can be found in several classic artificial intelligence textbooks. For our model, we have a heuristic (evaluation) function f (n) = g(n) + h(n)
Wireless signal transport control
Static and dynamic obstacles in the simulation terrain affect the radio signal transport. In commonly wireless networks, radio signals with frequencies above 800 MHz have extremely small wavelengths compared with the dimensions of obstacles, such as buildings and moving vehicles. This indicates that ray-optical methods can be used to describe the wireless signal propagation. There are several basic mechanisms affecting wireless signal propagation: reflection and transmission, diffraction, scattering[13] . It is assumed that the node does not move into the obstacles, and the obstacles prevent the wireless signal propagation. We can model wireless signal as demonstrated in Fig. 4. The wireless signal propagation distance is the length from node i to i2 , and there are several obstacles preventing wireless signal in terrain. For each node i and obstacle j, the wireless signal of node i cannot reach the shadow area, which is closed in the point set { i1 , i2 , i3 , i4 , a }; we define this shadow area as JamArea (i, j). Node i’s JamArea can be represented as follows: Set the number of nodes from 0 to N1 and the number of obstacles from 0 to N2 ; then N2 JamArea(i, j) (3) JamArea (node i) = Uj=0
(2)
where g(n) provides the path cost from the start node to node n, h(n) is the estimated cost of the cheapest path from n to the goal, and f (n) is the estimated total cost of the cheapest solution through n. We make the reversion for algorithm A*: when the expanding node puts nodes on OPEN list, it is necessary to insert according to the f value (order from small to big), and therefore, it is omissible to sort the OPEN list. OPEN list and CLOSED list are double chained lists, which can be allocated by dynamic memory management.
Fig. 4
Wireless signal propagation of node i around obstacle
As illustrated in Fig. 4, once another node enters JamArea (node i), transmission of the signal from node i is completely jammed. The core of the signal coverage calculations for any environment is the path-loss model, which relates the
Virtual reality mobility model for wireless ad hoc networks loss of signal strength to the distance between two nodes. VRMM uses both the Friis’ free space propagation model and the two-ray model, and the choice of the two models depends on the value of the crossover distance, dc . Let d represent the distance of two nodes. P r and P t are the transport powers; and Gt, Gr, lamda, L, ht, and hr are the relative parameters. The signal propagation algorithm can be given as in Fig. 5.
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dom waypoint model and OMM[14] . However, firstly, VRMM integrating with NS2[15] is a problem. 4.1
How VRMM integrates with NS2
For NS2 simulation, the two key inputs are nodemovement and traffic-connection file. The VRMM is used to create node-movement file. VRMM is a plugin to the NS-2 network simulator. The plugin has been developed for the ns-2.27 version of the network simulator. It is a tarball, which includes four directories: (1) setdest/
P r Propagation (dc, d) { if(d > dc) Pr =TwoRay(); else P r=Friis(); return P r; } Friis(double P t, double Gt, double Gr, double lambda, double L, double d) {
This directory contains files that generate the mobility scenario. This part includes the main VRMM implementation. (2) mobile/ (3) mac/ These directories contain files that will simulate the signal propagation model. This part includes the changes that VRMM brings to other. (4) scripts/
double M =lambda / (4 * P I * d);
This directory contains some scripts to create simulation scenarios.
return (P t*Gt*Gr*(M *M ))/L;
4.2
//Friis free space equation
} TwoRay(double P t, double Gt, double Gr, double ht, double hr, double L, double d) { //Two-ray ground reflection model. return P t*Gt*Gr*(hr*hr*ht*ht)/ (d*d*d*d*L); } Fig. 5
Wireless signal propagation
When d dc , we use the two-ray model; When d < dc , we use the Friis’ free space propagation model.
4. VRMM integration and network simulation The primary goal of the simulation is to evaluate the effect of the model in simulation environment. To evaluate the impact of our mobility model on the performance of an ad hoc routing protocol, we use the AODV ad hoc routing protocol, and also compare our results with the performance of AODV using ran-
Router protocol evaluation metrics
Four important performance metrics are evaluated: (1) Packet delivery fraction: The ratio of the data packets delivered to the destinations. (2) Control packet overhead: number of network layer control packet transmissions. (3) End-to-end delay of data packets: This includes all possible delays caused by buffering during route discovery latency, queuing at the interface queue, retransmission delays at the MAC, propagation, and transfer times. (4) Average hop (path length): the hops from source to destination. 4.3
Obstacle and simulation setup
The important input of the simulation is the obstacle parameter. The obstacle parameter will be introduced into the simulation in the production of the simulation scene. The simulation terrain is 1 000 m × 1 000 m terrain from a campus, and the maximum node transmission range is 250 m. Obstacle coordinates are listed as
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Yu Ziyue, Gong Bo & He Xingui lation, and therefore, the nodes follow the limited path and the product limited control overhead by router.
Table 1. Table 1 No.
Fig. 8 shows the router protocol end-to-end data
Obstacle coordinates
vertex1
vertex 2
vertex 3
vertex 4
(x1 , y1 )/m
(x2 , y2 )/m
(x3 , y3 )/m
(x4 , y4 )/m
1
132,168
132,564
500,564
500,168
2
660,580
660,950
818,950
818,580
3
154,704
154,960
532,960
532,704
4
704,205
704,900
820,900
820,205
The simulation experiment is conducted in the NS2 environment and uses the ad hoc networkextensions developed by CMU[15] . At the MAC layer, we use the IEEE 802.11 DCF protocol, and the bandwidth is 2 Mbps. We assume that all nodes are on the same horizon plane. There are 20 nodes in the simulation terrain; all nodes are assigned the same speed between 0 and 10 mps. The simulation runs for 500 s. After the simulation begins, 10 CBR data sessions are started. The data packet size is 512 bytes, and the sending rate is 5 packets per second. The node pause time is set to 60 s. 4.4
Fig. 6
Packet delivery fraction with varied mobility
Fig. 7
Control packet overhead with varied mobility
Result analysis
The router protocol simulation data packet received is presented in Fig. 6. When the node’s speed is low, the VRMM data packet received is smaller than the RWP model; when the node’s speed is high, the VRMM data packet received is equal to the RWP model. This result reveals that obstacles affect the propagation of the wireless signal when the speed is low. The VRMM data packet is always larger than the OMM data packet, and hence, VRMM is more effective than OMM in obstacle environment. It is due to that the static obstacles and dynamic obstacles prevent the wireless signal propagation. The router protocol simulation control overhead is shown in Fig. 7. The graph shows that the number of control packets transmitted by our model is larger than RWP and OMM. It is due to that there is not any obstacle in RWP environment, the router is no relation to the obstacles. In OMM, even the result considers the obstacle effect, but the obstacle’s Voronoi graph is created in the beginning of the simu-
Fig. 8
End-to-end latency with varied mobility
Virtual reality mobility model for wireless ad hoc networks packet delivery delay. When nodes move in high speed, the VRMM’s end-to-end delay is lesser than that in the RWP; however, VRMM and OMM have similar end-to-end delay. The router protocol simulation average hop (path length) is shown in Fig. 9. The VRMM’s average hop is similar to the RWP’s average hop; however, the VRMM’s average hop is larger than the OMM’s average hop.
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from VRLAB in Beijing University of Aeronautics and Astronautics for advices on collision detection of virtual reality, and also thank David Maltz from CMU for his email advices.
References [1] Broch J. A performance comparison of multi-hop wireless ad hoc network routing protocols. Maltz D A, Johnson D B, Hu Y C, et al. MobiCom, Dallas, Texas, 1998: 85–97. [2] Johnson David, Maltz David. Dynamic source routing in ad hoc wireless networks. In Mobile Computing. Kluwer Academic Publishers, 1996, chapter 5:153–181. [3] Jardosh A, Belding-Royer E M, et al. Real world environment models for mobile ad hoc networks. In the IEEE Journal on Special Areas in Communications-Special Issue on Wireless Ad hoc Networks, 2005, 23(3). [4] Bettstetter C. Smooth is better than sharp: a random mobility model for simulation of wireless networks. MSWIM, Rome, Italy, 2001: 19–27. [5] Camp T, Boleng J, Davies V. A survey of mobility models for ad hoc network research. Wireless Comm and Mobile Computing (WCMC): Special Issue on Mobile Ad Hoc Net-
Fig. 9
Average hop(path length)
working: Research, Trends, and Applications, 2002, 2(5): 483–502. [6] Jardosh A. Towards realistic mobility models for mobile
5. Conclusions
ad hoc networks. Belding-Royer E M, et al. Proc. of
The article presents a virtual reality mobility model based on collision detection, which can be used in ad hoc network simulation. The VRMM uses two new approaches for detection of obstacles and node path planning: to find obstacles using collision detection, and to make pathway planning using algorithm A*. This feature makes the movement of nodes in VRMM more smooth and intellective than the other mobility models. By comparing the simulation result between the VRMM and the random waypoint model, it is seen that the VRMM significantly impacts the performance of ad hoc network routing protocols. Several researches can be continued in the future; however, to incorporate dynamic obstacles into our model is a challenge. This article provides only a special scenario of simulation, and therefore, various useful scenarios of simulation need to be researched.
MOBICOM, San Diego, CA, 2003. [7] Guerin R A. Channel occupancy time distribution in a cellular radio system. IEEE Trans. on Vehicular Technology, 1987, 36(3): 89–99. [8] Royer E M. An analysis of the optimum node density for ad hoc mobile networks. Melliar-Smith P M, Moser L E. Proc. of the IEEE International Conference on Communications, Helsinki, Finland, 2001. [9] Haas Z J. The performance of query control schemes for the zone routing protocol. Pearlman M R. Proc. of SIGCOMM, Vancouver, British Columbia, 1998: 167–177. [10] Shi J Y. Virtual reality: foundation and application. China Science Press, 2002. [11] Hearn D, Baker M P. Computer graphics: C version. Prentice Hall, 1997, Chapter 9: 216–250. [12] Nilsson N J. Artificial intelligence, a new synthesis. Morgan Kaufmannn, 1998 Chapter 9: 139–160. [13] Pahlavan K, Krishnamurthy P. Principles of wireless net-
Acknowledgments
We appreciate Cheng Guojun
works: a unified approach. Prentice Hall, 2002.
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[14] Perkins C E, Royer E M. The ad hoc on-demand distance
Gong Bo completed his Ph. D. in BUAA. He is an
vector protocol. Perkins C E, editor. Ad hoc Networking,
associate professor of Institute of Command and Technology of Equipment. His research focuses on software
Addison-Wesley, 2000: 173–219. [15] NS2[EB/OL].
http://www.isi.edu/nsnam/ns/index.html,
engineering, etc.
2006.
Yu Ziyue was born in 1973. He is currently a Ph.
He Xingui is a professor in School of Electronics En-
D. student in School of Computer Science at Beijing University of Aeronautics and Astronautics (BUAA).
gineering and Computer Science at Peking University. His main research interests include intellectual com-
His research focuses on wireless networks, Internet security, etc. E-mail: [email protected]
putation, software engineering, etc. He is a member of Chinese Academy of Engineering.
Virtual reality mobility model for wireless ad hoc networks Yu Ziyue1 , Gong Bo1,2 & He Xingui3 1. School of Computer Science & Engineering, Beijing Univ. of Aeronautics and Astronautics, Beijing 100083, P. R. China; 2. Inst. of Command and Technology of Equipment, Beijing 101416, P. R. China; 3. School of Electronics Engineering and Computer Science, Peking Univ., Beijing 100871, P. R. China (Received May 10, 2007)
Abstract: For wireless ad hoc networks simulation, node’s mobility pattern and traffic pattern are two key elements. A new simulation model is presented based on the virtual reality collision detection algorithm in obstacle environment, and the model uses the path planning method to avoid obstacles and to compute the node’s moving path. Obstacles also affect node’s signal propagation. Considering these factors, this study implements the mobility model for wireless ad hoc networks. performance of protocols.
Simulation results show that the model has a significant impact on the
Keywords: wireless ad hoc networks, computer simulation, network protocols, mobility models.
1. Introduction Wireless ad hoc networks originate from military research and development, and recently have been used in several fields. Simulation plays an important role in the research of wireless ad hoc networks. In order to carry out an exhaustive research on the protocols of wireless ad hoc networks, all primary factors that affect the protocol performance must be considered. Fortunately, these primary factors have been simplified reasonably, and fall into two categories: the mobility model and the traffic model[1] . Since some wireless ad hoc network’s simulation models are simplistic, researchers find that there are several differences between the theoretical researches and the experimental results. Therefore, researchers proceed to consider other experimental aspects[2] . Currently, the random waypoint model (RWP) is one of the most popular mobility model for studying wireless ad hoc network protocol[2], and the latest important advance of this field is the obstacle mobility model (OMM)[3] . This article presents a mobility model based on virtual reality (VRMM, virtual reality mobility model). Firstly, the model synthesizes the random waypoint
model and the obstacle mobility model, and then introduces the virtual reality collision detection method to build a new mobility model. The mobility model can be used to solve two important issues. Firstly, this model uses the virtual reality collision detection method to process the position and time of the collisions; Secondly, based on random waypoint model and the obstacle mobility model, this model incorporates a collision detection methodology to process the interaction between nodes and obstacles. When a node meets an obstacle, the node plans its pathway using the artificial intelligent method. This model makes the movement of nodes more smooth and realistic than that done by other random models.
2. Research background There are several mobility models, which are used to analyze and simulate protocols of wireless ad hoc networks. This article uses the term “node” to denote any kind of network-enable device, for example, a user, a moving mobile terminal, or a device inside vehicle. Bettstetter[4] provides a better categorization and map of the mobile model, while a survey and comprehensive comparison of a variety of mobile models can
820 be found in Camp, et al [5] . A new special description of wireless ad hoc networks mobility model is given by Jardosh, et al[6] . Many of the analytical mobility models originate from some rather simple presumption, while models for simulation-based studies describe the movement of nodes in a slightly complicated manner. The remainder of this section focuses on several primary mobility models for wireless ad hoc networks. There exist several mobility models based on Guerin’s model[7] , which is commonly denoted as the random direction model. Several variations of the random direction model are proposed. Royer, et al[8] defined a simplified version of the random direction model, and the main difference is that every node moves until it reaches the boundary of the simulation terrain rather than moving only for some period of time. Hass, et al[9] put forward another version of the random direction model. Hong, et al also provided a group mobility model that has evolved from the random direction model.
Yu Ziyue, Gong Bo & He Xingui bility model. Before Jardosh and Belding-Royer presented the OMM, one common characteristic of all mobility models was that mobile nodes move in open, unobstructed terrain. OMM incorporates obstacles on the simulation terrain, and from this viewpoint, it is a more realistic model than the other earlier models. 3.1
VRMM framework
Commonly, the mobility model includes two functional blocks: motion constraints and traffic generator. When focusing on a macroscopic point of view, the virtual reality mobility model includes the following components: speed control, direction control, collision detection, path planning, and wireless signal transport control. A concept map for virtual reality mobility models can be depicted in Fig. 1. The solid part represents motion constraints, and the dashed part represents traffic generator.
Johnson, et al presented a random waypoint model, which was used in NS-2 network simulation tool, and has been one of the most widely used mobility model. Various performance evaluations of the mobile ad hoc network are based on this mobility model. The latest important advance of the mobility model for studying ad hoc network protocols is the obstacle mobility model presented by Jardosh, et al. This model can be stated as follows: Incorporation of obstacles to model the location of buildings within an environment, i.e., a college campus. Once the buildings are placed, this model uses the Voronoi diagram of obstacle vertices to construct movement paths; then nodes are randomly distributed across the paths and destinations are selected from the set of obstacles, and shortest path route computations are used to determine the path that each node will use to reach its selected destination. There are two important factors in OMM: the obstacles are all static, and in the beginning of the simulation, the paths for nodes movement are planned.
3. Mobility model and algorithm This section presents our model: virtual reality mo-
Fig. 1
VRMM framework
In the article, the virtual reality mobility model is divided into three parts to depict the following: (1) Incorporation of objects that can model static obstacles as well as dynamic obstacles. After there are obstacles in the simulation terrain, the mobility model uses the virtual reality collision detection method to find obstacles; (2) Use of artificial intelligent heuristic path search algorithm to plan a new pathway when any node finds obstacle in its pathway;
Virtual reality mobility model for wireless ad hoc networks (3) Determination of signal propagation owing to obstacles. 3.2
Speed and direction control
Modeling the speed behavior of nodes is based on the use of target speeds. A node moves with constant speed v until it arrives at a new target. Collision detection is an important research topic in several fields: computer simulation, virtual reality, computer animation, computer geometry, robotology, CAD/CAM[10] . There are several obstacles in the simulation terrain. When nodes move in this area, the nodes often meet obstacles. For the sake of keeping simulations realistic, it is necessary to detect the meet between nodes and obstacles, and then compute the response of nodes (collision detection). Thus, there are two problems: Firstly, to detect when and where the collision will happen; and secondly, to compute the node’s response after collision. Collision detection precision and real time are two important constraints. As for the real time of collision detection, the collision detection speed must be greater than the network simulation requirement, i.e., collision detection must be completed in network simulation event interval.
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There are several collision detection methods and algorithm libraries developed by researchers; however, many of these have pros and cons, and different usage. We can perform application requirement analysis based on three factors: model category, detection category, and scenario classification. As for the model category, since the simulation terrain can be considered as a planar and obstacles are disseminated in a planar, the planar model is satisfied. As for the detection category, it can be classified into precision and approximate collision detection. Thus, the model uses approximate collision detection. At last, since our model includes static and dynamic obstacles, the model uses dynamic scenario. The number of mobile nodes is set as m: N1 , N2 , N3 , . . ., Nm , and the number of obstacles is set as n: O1 , O2 , O3 , . . ., On . Tprev is the global time variable, and the model’s collision detection algorithm can be given as Fig. 3[10] . CheckForCollision (Tcurr, {N1 , . . ., Nm }, { O1 , . . ., On }) { //for each moving wireless node for(each Ni in { N1 , . . ., Nm }) do { //for each time interval for(t=Tprev to Tcurr, set step to dt) do {
Collision detection precision is determined by the application field. For networks simulation application, it is necessary to make approximate collision detection. When two objects not to collide each other, the model assumes that there is a collision between two objects; If there is only a little distance between two objects, and the distance is smaller than threshold value. An illustration of collision detection is given in Fig. 2.
//compute node position according time and //node velocity move node Ni to position according to time t; for(each Oj in { O1 , . . ., On }) do { //test node collides with obstacle if (Ni collides with Oj ) then reports that there is a collision at time t; } } Tprev = Tcurr; } } Fig. 3
Fig. 2
Moving nodes collide with obstacles
Collision detection algorithm
The model makes some improvements on the foundational collision detection algorithm: (1) Use of adaptive time step. The time loop step can be computed according to node speed and collision
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Yu Ziyue, Gong Bo & He Xingui
detection precision. The method is given as follows dt = CD P REC/speed
(1)
The result of algorithm A* is to obtain a pathway from start node to goal node without obstacle. 3.4
where speed denotes node’s speed, and CD PREC denotes collision detection precision. (2) Improvement of the approach for checking objects intersection. This can be done using application field, and tricks and tips[11] . (3) Integration of time loops. Since collision detection and network simulation both have time loops, the model must integrate the two time loops. 3.3 Pathway planning after collision detection There are two main purposes to make pathway planning: Firstly, to avoid obstacles; secondly, to search for the optimization pathway, which is often the shortest pathway[12]. Of course, there are various pathway planning methods. Some are traditional methods including steepest descent and optimum control, and it is difficult to use traditional methods in our model. Others are intellectual methods including dynamic programming, heuristic search, neural networks, simulated annealing and genetic algorithm etc. Dynamic programming and heuristic search are the popular methods in practice, but heuristic search is simple in theory, and the computing cost is smaller. We select algorithm A* of heuristic search to use in our model’s pathway planning. Algorithm A* can be found in several classic artificial intelligence textbooks. For our model, we have a heuristic (evaluation) function f (n) = g(n) + h(n)
Wireless signal transport control
Static and dynamic obstacles in the simulation terrain affect the radio signal transport. In commonly wireless networks, radio signals with frequencies above 800 MHz have extremely small wavelengths compared with the dimensions of obstacles, such as buildings and moving vehicles. This indicates that ray-optical methods can be used to describe the wireless signal propagation. There are several basic mechanisms affecting wireless signal propagation: reflection and transmission, diffraction, scattering[13] . It is assumed that the node does not move into the obstacles, and the obstacles prevent the wireless signal propagation. We can model wireless signal as demonstrated in Fig. 4. The wireless signal propagation distance is the length from node i to i2 , and there are several obstacles preventing wireless signal in terrain. For each node i and obstacle j, the wireless signal of node i cannot reach the shadow area, which is closed in the point set { i1 , i2 , i3 , i4 , a }; we define this shadow area as JamArea (i, j). Node i’s JamArea can be represented as follows: Set the number of nodes from 0 to N1 and the number of obstacles from 0 to N2 ; then N2 JamArea(i, j) (3) JamArea (node i) = Uj=0
(2)
where g(n) provides the path cost from the start node to node n, h(n) is the estimated cost of the cheapest path from n to the goal, and f (n) is the estimated total cost of the cheapest solution through n. We make the reversion for algorithm A*: when the expanding node puts nodes on OPEN list, it is necessary to insert according to the f value (order from small to big), and therefore, it is omissible to sort the OPEN list. OPEN list and CLOSED list are double chained lists, which can be allocated by dynamic memory management.
Fig. 4
Wireless signal propagation of node i around obstacle
As illustrated in Fig. 4, once another node enters JamArea (node i), transmission of the signal from node i is completely jammed. The core of the signal coverage calculations for any environment is the path-loss model, which relates the
Virtual reality mobility model for wireless ad hoc networks loss of signal strength to the distance between two nodes. VRMM uses both the Friis’ free space propagation model and the two-ray model, and the choice of the two models depends on the value of the crossover distance, dc . Let d represent the distance of two nodes. P r and P t are the transport powers; and Gt, Gr, lamda, L, ht, and hr are the relative parameters. The signal propagation algorithm can be given as in Fig. 5.
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dom waypoint model and OMM[14] . However, firstly, VRMM integrating with NS2[15] is a problem. 4.1
How VRMM integrates with NS2
For NS2 simulation, the two key inputs are nodemovement and traffic-connection file. The VRMM is used to create node-movement file. VRMM is a plugin to the NS-2 network simulator. The plugin has been developed for the ns-2.27 version of the network simulator. It is a tarball, which includes four directories: (1) setdest/
P r Propagation (dc, d) { if(d > dc) Pr =TwoRay(); else P r=Friis(); return P r; } Friis(double P t, double Gt, double Gr, double lambda, double L, double d) {
This directory contains files that generate the mobility scenario. This part includes the main VRMM implementation. (2) mobile/ (3) mac/ These directories contain files that will simulate the signal propagation model. This part includes the changes that VRMM brings to other. (4) scripts/
double M =lambda / (4 * P I * d);
This directory contains some scripts to create simulation scenarios.
return (P t*Gt*Gr*(M *M ))/L;
4.2
//Friis free space equation
} TwoRay(double P t, double Gt, double Gr, double ht, double hr, double L, double d) { //Two-ray ground reflection model. return P t*Gt*Gr*(hr*hr*ht*ht)/ (d*d*d*d*L); } Fig. 5
Wireless signal propagation
When d dc , we use the two-ray model; When d < dc , we use the Friis’ free space propagation model.
4. VRMM integration and network simulation The primary goal of the simulation is to evaluate the effect of the model in simulation environment. To evaluate the impact of our mobility model on the performance of an ad hoc routing protocol, we use the AODV ad hoc routing protocol, and also compare our results with the performance of AODV using ran-
Router protocol evaluation metrics
Four important performance metrics are evaluated: (1) Packet delivery fraction: The ratio of the data packets delivered to the destinations. (2) Control packet overhead: number of network layer control packet transmissions. (3) End-to-end delay of data packets: This includes all possible delays caused by buffering during route discovery latency, queuing at the interface queue, retransmission delays at the MAC, propagation, and transfer times. (4) Average hop (path length): the hops from source to destination. 4.3
Obstacle and simulation setup
The important input of the simulation is the obstacle parameter. The obstacle parameter will be introduced into the simulation in the production of the simulation scene. The simulation terrain is 1 000 m × 1 000 m terrain from a campus, and the maximum node transmission range is 250 m. Obstacle coordinates are listed as
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Yu Ziyue, Gong Bo & He Xingui lation, and therefore, the nodes follow the limited path and the product limited control overhead by router.
Table 1. Table 1 No.
Fig. 8 shows the router protocol end-to-end data
Obstacle coordinates
vertex1
vertex 2
vertex 3
vertex 4
(x1 , y1 )/m
(x2 , y2 )/m
(x3 , y3 )/m
(x4 , y4 )/m
1
132,168
132,564
500,564
500,168
2
660,580
660,950
818,950
818,580
3
154,704
154,960
532,960
532,704
4
704,205
704,900
820,900
820,205
The simulation experiment is conducted in the NS2 environment and uses the ad hoc networkextensions developed by CMU[15] . At the MAC layer, we use the IEEE 802.11 DCF protocol, and the bandwidth is 2 Mbps. We assume that all nodes are on the same horizon plane. There are 20 nodes in the simulation terrain; all nodes are assigned the same speed between 0 and 10 mps. The simulation runs for 500 s. After the simulation begins, 10 CBR data sessions are started. The data packet size is 512 bytes, and the sending rate is 5 packets per second. The node pause time is set to 60 s. 4.4
Fig. 6
Packet delivery fraction with varied mobility
Fig. 7
Control packet overhead with varied mobility
Result analysis
The router protocol simulation data packet received is presented in Fig. 6. When the node’s speed is low, the VRMM data packet received is smaller than the RWP model; when the node’s speed is high, the VRMM data packet received is equal to the RWP model. This result reveals that obstacles affect the propagation of the wireless signal when the speed is low. The VRMM data packet is always larger than the OMM data packet, and hence, VRMM is more effective than OMM in obstacle environment. It is due to that the static obstacles and dynamic obstacles prevent the wireless signal propagation. The router protocol simulation control overhead is shown in Fig. 7. The graph shows that the number of control packets transmitted by our model is larger than RWP and OMM. It is due to that there is not any obstacle in RWP environment, the router is no relation to the obstacles. In OMM, even the result considers the obstacle effect, but the obstacle’s Voronoi graph is created in the beginning of the simu-
Fig. 8
End-to-end latency with varied mobility
Virtual reality mobility model for wireless ad hoc networks packet delivery delay. When nodes move in high speed, the VRMM’s end-to-end delay is lesser than that in the RWP; however, VRMM and OMM have similar end-to-end delay. The router protocol simulation average hop (path length) is shown in Fig. 9. The VRMM’s average hop is similar to the RWP’s average hop; however, the VRMM’s average hop is larger than the OMM’s average hop.
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from VRLAB in Beijing University of Aeronautics and Astronautics for advices on collision detection of virtual reality, and also thank David Maltz from CMU for his email advices.
References [1] Broch J. A performance comparison of multi-hop wireless ad hoc network routing protocols. Maltz D A, Johnson D B, Hu Y C, et al. MobiCom, Dallas, Texas, 1998: 85–97. [2] Johnson David, Maltz David. Dynamic source routing in ad hoc wireless networks. In Mobile Computing. Kluwer Academic Publishers, 1996, chapter 5:153–181. [3] Jardosh A, Belding-Royer E M, et al. Real world environment models for mobile ad hoc networks. In the IEEE Journal on Special Areas in Communications-Special Issue on Wireless Ad hoc Networks, 2005, 23(3). [4] Bettstetter C. Smooth is better than sharp: a random mobility model for simulation of wireless networks. MSWIM, Rome, Italy, 2001: 19–27. [5] Camp T, Boleng J, Davies V. A survey of mobility models for ad hoc network research. Wireless Comm and Mobile Computing (WCMC): Special Issue on Mobile Ad Hoc Net-
Fig. 9
Average hop(path length)
working: Research, Trends, and Applications, 2002, 2(5): 483–502. [6] Jardosh A. Towards realistic mobility models for mobile
5. Conclusions
ad hoc networks. Belding-Royer E M, et al. Proc. of
The article presents a virtual reality mobility model based on collision detection, which can be used in ad hoc network simulation. The VRMM uses two new approaches for detection of obstacles and node path planning: to find obstacles using collision detection, and to make pathway planning using algorithm A*. This feature makes the movement of nodes in VRMM more smooth and intellective than the other mobility models. By comparing the simulation result between the VRMM and the random waypoint model, it is seen that the VRMM significantly impacts the performance of ad hoc network routing protocols. Several researches can be continued in the future; however, to incorporate dynamic obstacles into our model is a challenge. This article provides only a special scenario of simulation, and therefore, various useful scenarios of simulation need to be researched.
MOBICOM, San Diego, CA, 2003. [7] Guerin R A. Channel occupancy time distribution in a cellular radio system. IEEE Trans. on Vehicular Technology, 1987, 36(3): 89–99. [8] Royer E M. An analysis of the optimum node density for ad hoc mobile networks. Melliar-Smith P M, Moser L E. Proc. of the IEEE International Conference on Communications, Helsinki, Finland, 2001. [9] Haas Z J. The performance of query control schemes for the zone routing protocol. Pearlman M R. Proc. of SIGCOMM, Vancouver, British Columbia, 1998: 167–177. [10] Shi J Y. Virtual reality: foundation and application. China Science Press, 2002. [11] Hearn D, Baker M P. Computer graphics: C version. Prentice Hall, 1997, Chapter 9: 216–250. [12] Nilsson N J. Artificial intelligence, a new synthesis. Morgan Kaufmannn, 1998 Chapter 9: 139–160. [13] Pahlavan K, Krishnamurthy P. Principles of wireless net-
Acknowledgments
We appreciate Cheng Guojun
works: a unified approach. Prentice Hall, 2002.
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Yu Ziyue, Gong Bo & He Xingui
[14] Perkins C E, Royer E M. The ad hoc on-demand distance
Gong Bo completed his Ph. D. in BUAA. He is an
vector protocol. Perkins C E, editor. Ad hoc Networking,
associate professor of Institute of Command and Technology of Equipment. His research focuses on software
Addison-Wesley, 2000: 173–219. [15] NS2[EB/OL].
http://www.isi.edu/nsnam/ns/index.html,
engineering, etc.
2006.
Yu Ziyue was born in 1973. He is currently a Ph.
He Xingui is a professor in School of Electronics En-
D. student in School of Computer Science at Beijing University of Aeronautics and Astronautics (BUAA).
gineering and Computer Science at Peking University. His main research interests include intellectual com-
His research focuses on wireless networks, Internet security, etc. E-mail: [email protected]
putation, software engineering, etc. He is a member of Chinese Academy of Engineering.