In vehicular ad-hoc networks (VANETs), store-carry-forward approach may be used for data sharing, where moving vehicles carry and exchange data when they go by each other. In this approach, storage resource in a vehicle is generally limited. Therefore, attributes of data that have to be stored in vehicles are an important factor in order to efficiently distribute desired data. In VANETs, there are different types of data which depend on the time and location. Such kind of data cannot be deployed adequately to the requesting vehicles only by popularity-based rule. In this paper, we propose a data distribution method that takes into account the effective life and area in addition to popularity of data. Our extensive simulation results demonstrate drastic improvements on acquisition performance of the time and area specific data.
In an Intelligent Transportation System (ITS) [
In the above approach, as the amount of data items carried by a vehicle becomes larger, the performance such as the ratio of desired data reception is more improved. This is because a vehicle has many chances that it can encounter another vehicle which has desired data. Storage resource in a vehicle, in other words, the amount of saved data is limited in general. Therefore, attributes of data that should be carried by vehicles are an important factor in order to efficiently disseminate desired data. So far, there has been Roadcast study as a typical popularity aware content sharing scheme in VANETs. Roadcast consists of two components called popularity aware content retrieval and popularity aware data replacement. The popularity aware content retrieval scheme finds the most relevant and popular data for user’s query. The popularity aware data replacement ensures that different data is deployed inside a vehicular network according to its popularity. Roadcast achieves that more popular data tends to be shared with other vehicles so that the query delay and the query hit ratio can be improved. The existing methods such as Roadcast deploy the data to vehicles randomly according to its relative popularity. In VANETs, however, there are different types of data which depend on the time and location, and some information has an effective life or a deployment area. Such kind of data cannot be deployed adequately to the requesting vehicles only by popularity-based rule, and the data out of effective scope may not be a high valuable for requesting user even if it is obtained. Existing method based on popularity cannot achieve the system which takes into account the effective scope of the data. Therefore, in this paper, we propose a data distribution method that takes into account the effective life and area in addition to popularity of data.
The rest of this paper is organized as follows. Related work is discussed in Section 2. Section 3 describes our system model. Section 4 presents the effective life and area based data storing and deployment. Performance evaluations are shown in Section 5. Finally, we conclude the paper in Section 6.
Vehicular networks represent an interesting application scenario not only for traffic safety and efficiency but also for more commercial and entertainment support. So far, however, most of vehicular network researches focus on routing issues [
In the last couple of years there has been an increasing interest in in-network aggregation mechanisms for vehicular ad hoc networks [
On the other hand, content retrieval through intermittent contact opportunities in vehicular networks is also an important technique. In literature [
Studies in data replacement start from cache replacement. In literature [
Roadcast [
In Roadcast, the popularity of data is considered as the most important factor. However, effective life and area of the content is not taken into account. In general, various kinds of information are shared in VANET. This information include not only entertainment information such as MP3 music or video but also restaurant and parking information, sale advertisement of shops located on the roadside, and so on. Such information may be delivered only for specific areas or may have limited valid time. In other words, the value of the information may be reduced or become invalidout side effective life and area. When such content is disseminated, not only popularity of the content but also effective life and area should be taken into account. In the paper, we propose the data deployment based on the effective life and area as well as popularity of data.
In vehicular ad-hoc networks (VANETs), moving vehicles carry data and exchange it as they pass each other. In this section, we describe our system model. In our system, a vehicle obtains data from the wireless network infrastructure. A vehicle with obtained data moves on a road and encounters another vehicle on the opposite lane. Then the vehicle exchanges the obtained data each other. The vehicle obtains the desired data by repeating this behavior.
In
In vehicular ad-hoc networks (VANETs), moving vehicles carry data and exchange it as they pass each other. Storage resource in a vehicle, in other words, the amount of saved data is limited in general. Therefore, attributes of data that should be carried by vehicles are an important factor in order to efficiently disseminate de-
sired data. In this paper, we focus on effective life and area as well as popularity of data, and propose a replacement algorithm based on these attributes. The proposed method decides the data to be replaced according to the following operations.
In this method, the data which has the largest wi, defined as Equation (1), has to be replaced from the storage of the vehicle, if new data is input when the storage capacity is full.
where fi, gi and ci are popularity, generation number of data and the replication count of data i, respectively. Ti and Di are calculated based on effective life and area of its data.
Popular data should not be replaced if it has not been disseminated yet, in order to give more opportunities to disseminate the data with higher popularity. In our method, therefore, each data source decides whether it deploys a specific object or not with a probability which is predefined by its popularity. When the total number of vehicles is V and the request probability of data i is Pi, the expected number of vehicles which obtain data i is VPi. This means that the data is deployed randomly in a VANET so that the number of replicated data is linear to its popularity. Furthermore, in our method, it can be expected that data with low popularity is also deployed to vehicles because of the probabilistic manner. In our system, it is also necessary to collect popularity information of all data which is expected to be requested from users.
The generation number gi corresponds to the number of vehicles which are transferred from its original source. The replication count ci represents the number of copies which are generated by the same vehicle. If the data has large gi or ci, it can be expected that many copies of the data exist in the VANET. Therefore, this system is willing to replace the data which has higher gi and ci to avoid deployment of redundant copies and store other kinds of data. is the coefficient that decides which gi or ci is more important.
Ti represents a temporal effectiveness of data i and is obtained by normalizing the age of data i to adjust the scale of the other elements. Ti is calculated by the following equation.
If Ti < 1, the age of data i is within the effective life, otherwise the data i grow stale.
Di indicates a spatial effectiveness of data i, similarly with temporal effectiveness. Di is expressed by the following.
There are different possible types of functions for Ti and Di function. In this paper, we use above function as one example in which the effectiveness decays linearly with time and distance.
In VANET where vehicles move at high speed, since the time for the data exchange is less, the amount of data which can be transmitted and received at a time is also limited. Thus, the performance of our system depends on the scheduling method of data deployment. In the paper, we proposed the following scheduling method.
When a vehicle encounters the other vehicle, our proposal prefers to deploy the data which has the smallest wi, defined as Equation (1). It can be expected that popular data within effective life time and area is distributed faster.
In this section we describe the simulation model and compared model for our system evaluation, and then present the simulation results.
To investigate the performance of our deployment, we evaluate probability of data reception, i.e., the percentage of data items which requesting vehicles successfully obtained. In our evaluation, we implement our deployment on the ONE simulator (ver.1.4.0) [
In this evaluation, we compare the performance of the proposed method with popularity-only which decides the data to be deployed according to its popularity. In the popularity-only, the data which has the largest
quired data. From
Furthermore, we also evaluate the performance of our proposal in terms of effective data.
Parameter | Value |
---|---|
Simulation Time | 12 h |
Number of Vehicles | 150 |
Simulation Area | 4.5 km ´ 3.4 km (Helsinki area) |
Communication Range | 200 m |
TransmissionSpeed | 250 KBps |
Data Size | 500 KB - 2 MB |
Size of Buffer Memory | 5 MB - 40 MB |
Keyword Set Size | 40 |
Number of Keywords in Data | 2 - 8 |
Number of Keywords in Query | 3 - 5 |
Effective Life of Data | 0.5 h - 3 h |
Effective Range of Data | 500 m - 2 km |
Zipf Parameter | 0.5 |
Vehicle Speed | 10 m/s - 15 m/s |
α value | 0.5 |
We also evaluate effective data ratio in its effective area according to elapsed time from data generation. We set the parameters as shown in
In
Parameter | Value |
---|---|
Size of Buffer Memory | 15 MB |
Effective Range of Data | 500 m |
Effective Life of Data | 0.5 h, 1 h, 1.5 h |
proposal to distribute more valid data to the effective area within the effective life of data. In the proposed method, data is gradually deleted from vehicles’ buffer before its expiration. Thus, our proposal can suppress that the vehicle buffer is occupied by invalid data, and release the useless buffer resources of the vehicle for other more valid data. Therefore, area-limited information can be distributed to most effective vehicles by our proposal method.
In this section, we compare the performance on each factor of replacement policy.
effective life, and considering all factors (proposal). As the size of buffer memory becomes large, there is no difference in the buffered data, and all policy shows similar performance. However, in the case where the size of buffer memory is small, it is possible for the proposed method to consider the expiration time and scope of data at the same time, and achieve performance improvement.
In this section, data acquisition performance is evaluated when a parameter α in Equation (1) is changed.
In VANET where vehicles move at high speed, since the time for the data exchange is less, the amount of data which can be transmitted and received at a time is also limited. In this section, in order to investigate the differences in performance of the scheduling of deployment, we evaluate the performance of our proposal in comparison with the following scheduling method. In this evaluation, we have adopted our proposed replacement to both scheduling methods.
(FIFO) The data to be transmitted is decided by FIFO. Neither the expiration time nor scope of the data is taken into account. The data is transmitted according to the order of old data which the vehicle stored.
The above method (FIFO) is evaluated in the same manner as
In this paper, we propose a deployment method which can help a user get the useful data as much as possible through intermittently connected VANET. In VANETs, store-carry-forward approach may be used for data sharing, where moving vehicles carry and exchange data when they go by each other. In this approach, attributes of data that have to be stored in vehicles are an important factor in order to efficiently distribute desired data. In VANETs, there are different types of data which depend on the time and location. Such kind of data cannot be deployed adequately to the requesting vehicles only by popularity-based rule, and the data out of effective scope, such as effective life and area, may not be a high valuable for requesting user even if it is obtained. Existing method based on popularity cannot achieve the system which takes into account the effective scope of the data. We focus on effective life and area as well as popularity of data, and propose a data replacement algorithm of
full buffer inside a vehicle and also scheduling method of data deployment from vehicle based on these attributes.
From our simulation results, it is possible for our proposal to distribute more valid data to the effective area within the effective life of data. In the proposed method, data is gradually deleted from vehicles’ buffer before its expiration. Our proposal can suppress that the vehicle buffer is occupied by invalid data, and release the useless buffer resources of the vehicle for other more valid data. Thus, our proposal can greatly improve acquisition performance of the time and area specific data.
In the future, it is necessary to set up a proper effective life and area according to the kind of content.
HirokazuMiura,HidekiTode,HirokazuTaki, (2015) Effective Life and Area Based Data Storing and Deployment in Vehicular Ad-Hoc Networks. Communications and Network,07,146-157. doi: 10.4236/cn.2015.73014