Abstract—Data gathering and localization algorithms for vehicular ad hoc networks (VANets) are envisioned as key technologies for the near future. These technologies will pave the way for a number of potential applications that required precise positioning information that GPS systems are not able to provide. However, high mobility of vehicles in a VANet introduces frequent topology changes that negatively affect existing solutions and poses significant challenges to developing effective localization and data gathering mechanisms. In this paper, we propose a new data gathering and localization dissemination protocol that uses an existing time-space synchronization technique to efficiently collect data from heterogeneous networks composed of wireless sensor nodes and vehicles in a VANet. Localization information is disseminated from vehicles to sensor nodes, which can compute their positions without the need of energy-hungry GPS modules. The focus of this paper is to provide a mechanism that is able to simultaneously collect data from the nodes, relay data to interested nodes, and to disseminate localization information that will aid the nodes to estimate their positions.

Index Terms—Localization, Vehicular Ad hoc Networks, Wireless Sensor Networks, Mobile Data Gathering.

I.INTRODUCTION

number of potential and interesting applications of road

safety and intelligent transport systems have accelerated

the research and development of the emerging Vehicular Ad

Hoc Networks (VANets) [1–5]. In a VANet, vehicles have

communication capabilities and act independently to form an

ad hoc network. Vehicles in a VANets can also communicate

to a roadside infrastructure that might provide a plethora of

different applications that require an infrastructure. Such

applications can vary from advertisements and tourist

information along roads, to road safety that provides

information about road conditions, traffic, accident reports,

law enforcement, just to mention a few. However, most of

this kind of applications relies on precise localization systems

to provide its services in its integrity. Existing localization

systems such as GPS do not provide the level of precision

A

�This work is partially funded by NSERC Strategic Program, Canada Research Chair Program ,ORF Research Funds, EAR Research Award, and OCE.

required by most of the VANet applications. GPS accuracy

ranges from 20 to 30 meters, which limits the use of GPS to a

few applications, such as localization on a map. Road safety

or intelligent driving assistants require much higher accuracy.

Another drawback of GPS systems is that they do not work

indoors. Some other approaches try to address the lack of

accuracy and indoors support: Dead Reckoning assumes that

vehicles will continue performing the same movement and

estimate future positions; Cellular Localization utilizes the

technique employed by cellular networks to provide relative

position to mobile devices.

In this paper, we investigate and design a new data

gathering and location dissemination protocol for a hybrid

network of wireless sensor nodes an vehicles (VANets). Our

approach assumes the use of an existing localization

technique that solves time synchronization and localization

update simultaneously. This time-space synchronization

technique plays an important role in Wireless Sensor

Networks (WSNs) and VANets as most of the applications

require a certain degree of localization of nodes to be able to

identify from where data is coming. We propose to extend our

existing mobile data gathering mechanism for WSNs in order

to incorporate a time-space synchronization technique and be

able to collect data from the network and update nodes’

position simultaneously.

II.RELATED WORK

In this section, we discuss on the existing protocols and

techniques for data dissemination in wireless sensor networks

and VANets that use vehicles as data mules. In [6], a simple

experimental evaluation is performed in which the authors

consider only one mobile sink (or data mule) that moves

through a straight line while collecting data from the sensor

nodes. This approach reduces the number of hops a packet has

to travel in order to reach the sink and it also saves energy and

increases network lifetime. The authors employ the Directed

Diffusion [7][8] as the routing protocol. The sink broadcasts

an interest message to the neighboring sensor nodes while

moving through the straight line. The sensor nodes will

receive the interest message and possibly forward to their own

Efficient Data Gathering and Position

Dissemination Protocols For Heterogeneous

Vehicle Ad hoc and Sensor NetworksHoracio Fernandes, Azzedine Boukerche, Richard Pazzi, Samer Samarah

PARADISE Research Laboratory

SITE - University of Ottawa - Canada

neighbors. Each sensor node will start transmitting data to the

mobile sink when an event matches the sink's interest. This

work has been extended in [9] to support multiple data mules.

The drawback of these approaches is that they show high

latency in order to collect data and serve the application

because sensor nodes have to wait to transmit data until a data

mule is nearby. In [10], the authors explore predictable sink

mobility in order to save energy in a WSN. They propose a

simple sink-driven communication protocol in which the sink

is placed on a public transportation vehicle, and they assume

that the sink traverses the same path through a wide area

sensor network. Data is pulled by the sink by waking up the

sensor nodes based on proximity. Sensor nodes listen to the

wireless channel periodically in order to check if there is any

sink nearby. In a first cycle, the sensor nodes only observe

how often the sink comes within range and for how long. In a

second cycle, after the sink have broadcasted its beacon

message, each node within range will start a collision-

resolution based on 802.11 CSMA/CA, and it will transmit a

packet to the sink that contains the position of the node and

measurements collected during the first cycle. Sensor nodes

predict when the sink is likely to be nearby and start listening

to the wireless channel. This mechanism has a positive effect

on energy savings because the nodes can be in sleep mode

when the sink is not in range. The drawback of this approach

is that its application is limited to the assumption that the sink

traverses the network through the same path. The work

presented in [11] aims at extending the Directed Diffusion

protocol [7] in order to support sink mobility. The technique

performs a handoff scheme that follows a make-before-break

strategy similar to the soft-handoff schemes used in cellular

networks [12]. The approach presented in [13] consists of the

design of a robomote, a robot platform (hardware and

software) that functions as a single mobile node in a mobile

sensor network for a number of different applications. For

instance, it can be used to recover network connectivity by

having a few mobile nodes moving to desired locations and

repairing partitioned networks. In addition, it can be used to

distribute traffic load and energy consumption by moving

mobile sinks to different areas. The authors in [14] consider

the problem of positioning mobile cluster heads in order to

perform load balancing in a hybrid wireless sensor network. It

is a cluster-based approach in which each cluster head

acquires the positions and connection patterns of all sensor

nodes in the cluster. In addition, the cluster head tests the

possible positions it can move to by estimating the lifetime of

the nodes in the case the cluster head moves to that position.

The cluster head chooses the position where the maximum

lifetime was estimated and moves to that area.

Since VANets present highly dynamic topology changes

and frequent network fragmentation, routing protocols [15]

very often use position information in order to improve their

performance. Position-aided techniques have been used in Ad

Hoc networks [16–18] and can be applied to VANets with no

or little modifications. Greedy Forwarding [17][18], in which,

location information is used to relay data to a neighbor node

close to the destination node. Geographic routing protocols

designed specifically for VANets have also been proposed in

the literate [19][20]. Routing techniques have also been used

to provide access to road-side network infrastructures.

Localization techniques are used to improve data routing or

predict handoffs. Several applications that can benefit from

localization techniques have been investigate in recent years.

Such applications can, for instance, warn vehicles’ drivers and

occupants about road and traffic conditions, accidents, and

nearby services.

III.OUR PROPOSED MOBILE DATA GATHERING TECHNIQUE

In this section, we present our mobile data gathering

technique that aims at providing improved data dissemination

and position information simultaneously to sensor nodes and

vehicles in a VANet.

A.Brief Overview of the Time-Space Synchronization Algorithm

In this paper, we apply our localization technique proposed in

a previous work [1]. In this technique, the time-space

localization algorithm uses mathematical ranking and

weighting techniques to enhance the time computation

process. The algorithm takes advantage of the fact that both

synchronization and positioning problems are solved together,

thus saving resources and resulting in better performance for

both problems. In this case, the main idea of this technique is

to use the already synchronized clocks of the beacon nodes to

start the synchronization process at several locations at the

same time. In addition, GPS inaccuracy is not a problem and

the proposed algorithm is less affected by one hop

synchronization inaccuracy.

B.The Mobile Data Gathering Protocol

The objective of this paper is to investigate and design a

protocol for hybrid WSNs and VANets using vehicles as

mobile data collectors. In this work, we consider the

deployment of N wireless sensor nodes and M mobile data

collectors MDCs (vehicles) in a square area A. The MDCs

move at a maximum speed of S m/s and are capable of

communicating with wireless sensor nodes. A sensor node

utilizes its wireless radio resources to communicate with its

one-hop neighbors, and the sensor nodes have no global

knowledge of the network. Each sensor node is assumed to be

static and have transmission range of R meters. The sensor

nodes relay data to vehicles in a single-hop or multi-hop

fashion, depending on their distance from the closest vehicle.

In this approach, we have modified our previous mobile

data gathering protocol [2] to support the dissemination of

localization data among the sensor nodes. In essence, vehicles

will disseminate their positions using the localization

technique discussed in the previous section. Each vehicle will

also play the role of a cluster-head for the sensor network.

Each vehicle first starts the cluster configuration phase so that

all nodes joining the cluster will acquire the proper routing

information about how to reach the nearest vehicle. Each

sensor node holds a hop level hm variable that represents the

number of hops to reach the closest vehicle M. If there are

multiple vehicles in the neighborhood of a sensor node, this

sensor node will decide to forward DATA packets to the

vehicle that carries the smallest hm, i.e. the closest vehicle in

number of hops. An overview of the proposed protocol is

shown in Figure1. The algorithms are discussed in the next

section.

C.Data Dissemination Algorithm

An MDC starts a cluster configuration phase by sending a

BEACON message. A hop level hm is assigned to each sensor

node in the cluster, which can be seen as a tree rooted at the

MDC. The sensor node that receives a beacon will first check

its internal hop level hm and the hop level h of the received

message. Thereafter, it forwards the message when

appropriate. When a sensor node receives a BEACON

message, it first checks the MDC's signal strength SS before

performing any routing updates. Each MDC broadcasts a

BEACON message periodically at every TB seconds. The

BEACON packet contains the MDC id, time to live TTL, a

hop level h, and the GPS position of the MDC. The entire

process of routing updates is described in Figure 1. We have

specified a reception threshold RxTHRESH based on the

communication range R of the node. Based on the signal

strength, the node will react according to the following rules:

• IF (SS ≥ RxTHRESH): the node is receiving strong signal

from the MDC. Then, the node detects if the sender of the

BEACON is its current MDC (based on MDC’s id). If the

BEACON message was sent from the node's current

MDC, the node does not need to perform any routing

changes. It will only update the SScurrent = SS, in which

SScurrent represents the most up-to-date signal strength

received from its current MDC, and SS is the signal

strength measured from the received BEACON message.

The SScurrent variable is kept by the sensor node and it will

be used when replying to other nodes that might request a

route to an MDC; The sensor node then checks IF (id ≠

idcurrent): if so, the beacon was sent from a different MDC.

In this case, the node silently drops the packet because it

is already a member of a cluster. In case the node does

not belong to any cluster or in the case the node already

belongs to a cluster but (hm > h), which means that there

is a shorter path to an MDC, the node joins the MDC's

cluster by updating its routing table and state variables.

Thereafter, the node broadcasts a CLU_CFG message to

its neighbors if TTL has not reached 0.

• IF (SS ≥ RxTHRESH): the node is within the communication

range of an MDC, but the signal strength is below the

allowed threshold. This can have two meanings: the

MDC is moving away from the node; or the MDC has

just entered the node's communication range. Therefore,

the node must find out the context of this BEACON

message; In the case id = idcurrent, the originator of the

beacon was the node's current MDC, but the node is

receiving low signal strength. Therefore, the node must

update its routing information and start looking for

another MDC. The node removes the routing entry for

that MDC and updates its state parameters. Usually, if the

MDC is moving out of the node's range, the node will

probably find out that a neighbor has a route to that same

MDC. The process of finding another route to an MDC is

initiated by the node broadcasting a Low Signal Strength

message LSS. An LSS message also informs other nodes

that its originator node does not have a route to that

specific MDC anymore. The LSS message is sent only

after there is a change in the node's routing information.

For an illustration of this process, check Figure 1(b) and

1(c), in which nodes A and B receives a beacon with low

signal strength. Thus, nodes A and B broadcast an LSS

message to search for another MDC and also to inform

their neighbors that they have lost their routes to the

MDC;

After a sensor node receives a BEACON message, it might

send a CLU_CFG message. A sensor node that receives a

CLU_CFG message decides whether to join the cluster. For

that, the node will check if it already belongs to a cluster or if

it only needs to update its routing information. The sensor

node is able to determine what to do next by checking its hop

Figure 1. An overview of our proposed technique

level hm according to the following rules:

• hm = -1: the node joins the cluster and creates an entry

by setting up the address of the CLU_CFG message

originator as the destination to the MDC in its routing

table, as depicted in Figure 1(c) and 1(d). Node C

broadcasts a CLU_CFG message and its neighbors join

the cluster;

• hm > h: the sensor node updates its routing

information and its parameters. Thereafter, it forwards

CLU_CFG if TTL has not reached 0;

• otherwise: the node has already the shortest path to an

MDC. It silently drops the packet.

After receiving a BEACON and the measured signal

strength SS is below the reception threshold RxTHRESH, the

sensor node broadcasts a Low Signal Strength (LSS)

notification message. When a sensor node receives a

BEACON message, it sets a timeout timer for receiving the

next BEACON. If this timeout expires, the sensor node needs

to find another route by sending an LSS message. Upon

receiving an LSS message, the node verifies if the source

node is in its routing table as a destination to reach an MDC,

so as to decide if it should update its routing information.

Localization information is disseminated through the

network via the vehicles (MDC’s) encapsulated in a

BEACON message. Sensor nodes that receive the BEACON

will estimate and update their new positions based on the

position informed in the BEACON message. When a node

receives BEACON messages from three or more MDCs or

from the same MDC, but at different positions, the node can

estimate its own position using the techniques discussed in

[1]. In our initial investigation, we used triangulation to

compute the position. The node that has estimated its position

will also disseminate its localization information to other

nodes within CLU_CFG messages. Thereafter, the other

nodes in a cluster will be able to estimate their position using

the same strategy.

IV.CONCLUSIONS AND FUTURE WORK

Mobility support and localization for vehicular ad hoc

networks are challenging issues that are still open to debate.

High mobility and error-prone wireless channels pose

significant difficulties to the development of efficient

solutions.

In this paper, we have investigated and proposed a new

data gathering and localization dissemination protocol for

WSNs and VANets that uses vehicles as mobile data

collectors. In essence, vehicles will be responsible for

collecting data through the sensor network and from other

vehicles as well, and update their localization information

simultaneously. We have combined a time-space

synchronization technique and a mobile data collector

mechanism for sensor networks and applied it to vehicular ad

hoc networks. We have modified our previous mobile data

collector mechanism for wireless sensor networks and applied

to a heterogeneous network of wireless sensor nodes and

vehicles in a VANet. We are currently implementing the

protocols and their performance results are subject of future

work.

REFERENCES

[1] Boukerche, A.; Oliveira, H.A.B.; Nakamura, E.F.; Loureiro, A.A.F. A

Novel Location-Free Greedy Forward Algorithm for Wireless Sensor Networks. Proceedings of the (ICC 2008), 2008. p. 2096-2101.

[2] Pazzi, R. W., Boukerche, A.: Mobile data collector strategy for delay-

sensitive applications over wireless sensor networks. Computer Communications 31(5): 1028-1039 (2008)

[3] S. Biswas, R. Tatchikou, F. Dion, Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety, IEEE Communications Magazine 44 (1) (2006) 74–82.

[4] J. Blum, A. Eskandarian, L. Hoffman, Challenges of inter-vehicle ad hoc

networks, IEEE Transactions on Intelligent Transportation Systems 5 (4) (2004) 347–351.

[5] W. Kiess, J. Rybicki, M. Mauve, On the nature of inter-vehicle communication, in: WMAN 2007: Proceedings of the 4th Workshop on Mobile Ad-Hoc Networks, 2007, pp. 493–502.

[6] J. Luo, J.-P. Hubaux, A survey of inter-vehicle communication, Tech. Rep. IC/2004/24, EPEL, 2004.

[7] J. Hubaux, S. Capkun, J. Luo, The security and privacy of smart vehicles, IEEE Security and Privacy Magazine 02 (3) (2004) 49–55.

[8] Kansal, A., Somasundara, A. A., Jea, D. D., Srivastava, M. B., and

Estrin, D. 2004. Intelligent fluid infrastructure for embedded networks. ACM MobiSys '04, pp. 111-124

[9] Intanagonwiwat, C., Govindan, R. and Estrin, D. "Directed diffusion: A scalable and robust communication paradigm for sensor networks, " ACM MobiCOM '00 , pp. 56--67.

[10] Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., and Silva,

F. 2003. Directed diffusion for wireless sensor networking. IEEE/ACM Trans. Netw. 11, 1, 2-16, Feb. 2003.

[11] Jea, D., Somasundara, A. A. and Srivastava, M. B. Multiple Controlled Mobile Elements (Data Mules) for Data Collection in Sensor Networks. In DCOSS, pages 244257, 2005.

[12] Chakrabarti, A., Sabharwal, A. and Aazhang, B. Using predictable observer mobility for power efficient design of sensor networks. In The second International Workshop on Information Processing in Sensor Networks, IPSN 2003, USA, pp. 129-145, April 22-23, 2003.

[13] Choksi, A., Martin, R. P., Nath, B. and Pupala, R. Mobility Support for Diffusion-based Ad-Hoc Sensor Networks. Rutgers University, Department of Computer Science, DCS-TR-463, April 2002.

[14] Harte, L., Hoeing, M., Mclaughlin, D. and Kta, R. K. CDMA IS-95 for Cellar and PCS, Mcgraw-Hill, 1999.

[15] Dantu, K. et. al. "Robomote: nabling mobility in sensor networks". In Information Processing in Sensor Networks, 2005. IPSN 2005. Fourth International Symposium, pages: 404- 409, 15 April 2005.

[16] Ma, M. and Yang, Y. 2006. Clustering and load balancing in hybrid sensor networks with mobile cluster heads. Proc. QShine '06.

[17] J. Chennikara-Varghese, W. Chen, O. Altintas, S. Cai, Survey of routing protocols for inter-vehicle communications, in: 3rd Annual International Conference on Mobile and Ubiquitous Systems - 2006, pp. 1–5.

[18] Y.-B. Ko, N.H. Vaidya, Location-aided routing (LAR) in mobile ad hoc networks, in: Mobile Computing and Networking, 1998, pp. 66–75.

[19] B. Karp, H.T. Kung, Gpsr: Greedy perimeter stateless routing for wireless networks, in: ACM Mobicom , 2000, pp. 243–254.

[20] Y. Yu, R. Govindan, D. Estrin, Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks, Tech. Rep. CSD-TR-01-0023, UCLA CS, 2001.

[21] J. Tian, L. Han, K. Rothermel, Spatially aware packet routing for mobile ad hoc inter-vehicle radio networks, in: ITS’03: IEEE Intelligent Transportation Systems, vol. 2, 2003, pp. 1546–1551.

[22] C. Lochert, H. Hartenstein, J. Tian, H. Fussler, D. Hermann, M. Mauve, A routing strategy for Vehicular Ad Hoc Networks in city environments, in: IVS’03: IEEE Intelligent Vehicles Symposium, 2003, pp. 156–161.