Vehicular Social Networks (VSNs) is the bridge of social networks and Vehicular Ad-Hoc Networks (VANETs). VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication protocols. Vehicular Named Data Networking (VNDN) is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations. However, content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’ high mobility. Our aim with this paper is to improve content delivery and cache hit ratio, as well as decrease the transmission delay between end-users. In this regard, we propose a novel hybrid VNDN-VSN forwarding technique based on social communities, which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area. Furthermore, we introduce an effective caching mechanism by dividing the content store into two parts, one for community private contents and the second one for public contents. Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN, in terms of data delivery ratio, average data delivery delay, and cache hit ratio.
Vehicular Ad Hoc Network (VANET) has been envisioned as one of the most promising research areas in the last decade. It aims at improving drivers’ safety and creating a comfortable driving environment. Besides, it allows inter-personal communication and entertainment for drivers and passengers. VANET features three main communication models: (i) Vehicle-to-Vehicle (V2V), it enables vehicles to connect directly with each other. (ii) Vehicle-to-Infrastructure (V2I), in this model vehicles are allowed to exchange and store data in central or distributed infrastructures. (iii) In the Hybrid model, vehicles are enabled to connect with distant vehicles and infrastructure nodes using multi-hop communication scheme. Due to the advances in mobile industries and social media, VANETs know a new research direction giving birth to the first social characterized vehicular network, referred to as vehicular social network (VSN) [
However, current VSNs are based on the traditional host-centric TCP/IP architecture that requires connection establishment between communication end-points before starting the data retrieval process [
Another research direction that has attracted researchers’ and industries’ eyes is Named Data Networking (NDN) [
Several studies have investigated NDN deployment in VANETs (VNDN) [
Despite the advantages of each solution, broadcast communication under high mobility conditions can result in serious packet flooding issues such as packet collisions and redundancy [
We aim to improve content delivery and cache hit ratio, as well as decrease the transmission delay between end-users. Hence, we propose in this paper a novel approach called “a Socially-Aware Forwarding Technique in Vehicular Named Data Networking” (SAFT-VNDN). In our proposal, we introduce the social community concept into VNDN. A social community in our work represents a group of vehicles (drivers or passengers) that share similar interests or visit the same destinations. For instance, football club fans, taxi drivers, travel clubs, cinema fan communities, etc., are interested in the same news and generally request the same data or videos. Thus, we have proposed:
An opportunistic caching scheme that divides the content store (CS) into two parts, one for community contents and the second one for general contents. Such a mechanism can optimize the data delivery and the cache-hit ratios. A forwarding scheme that relies on selecting the most suitable forwarder from community memberships. This can reduce the interest packet broadcast problems while increasing the probability to find data among community members.
The remainder of the paper is organized as follows: Section 2 provides an overview of Vehicular Named Data Networking VNDN. Section 3 describes the network model and the details of our proposed SAFT-VNDN approach. In Section 4, we present the simulation setup and discuss the achieved performance. Finally, Section 5 concludes the paper.
The deployment of NDN over VANET as an alternative to the traditional TCP/IP communication model has conceived a new hybrid network, which is called Vehicular Named Data Networking (VNDN) [ Consumer: it initiates the content fetching process by broadcasting an interest packet in the network. Producer: it makes content available in the network by generating and advertising them. It also replies by a data packet containing the requested content in response to a previously received Interest packet. Forwarder: it transmits (forwards) interest or data packet until they reach their final destination, either a producer or a consumer respectively. Data-mule: it carries contents previously received while moving without having network connectivity. This feature is allowed to VNDN vehicles thanks to its caching data structure referred to as Content Store (CS). Such a content store is used to save the received data packets to satisfy future requests.
As depicted in Pending Interest Table (PIT): PIT is indexed by content names extracted from the previously received interest packet but was not satisfied yet by the network. The PIT also records the incoming and outgoing interfaces for the pending Interests. Forwarding Information Base (FIB): This is similar to the classical TCP/IP routing table except that it is indexed by content hierarchical names rather than IP addresses. Content Store (CS): it is used to store the incoming data packets so that the subsequent requests can be accommodated.
Packet forwarding in VNDN can be divided into two phases based on the type of packet being forwarded Interest Packet Forwarding: To retrieve a specific content from the network, a VNDN node acts as follows:
– The consumer broadcasts an interest packet. (Vehicle A in – Upon receiving the interest packet, an intermediate vehicle (e.g., vehicle D in If the desired content exists, the intermediate vehicle sends back a data packet towards the consumer. It checks its PIT table for a possible entry. If there is a corresponding entry, it discards the interest packet immediately (because it has already forwarded the same interest packet). Otherwise, it adds a new entry in the PIT table and forwards the interest packet after performing a FIB lookup to find the most suitable next-hop. Data packet retrieving: Upon receiving a data packet, a vehicle first checks its PIT for a corresponding entry for the content name included in the data packet. If a name matches, it saves a copy in its local CS and forwards the packet immediately to all incoming interfaces aggregated in the matching PIT entry (vehicles C, D, E, F in figure
In this section, we present the details our proposed SAFT-VNDN, which is a novel hybrid VNDN-VSN forwarding technique. The key contribution of SAFT-VNDN is the solution it provides to improve content delivery and cache hit ratio while also reducing transmission delay between end-users. We believe that the establishment of social communities between vehicles in urban environments will increase the probability of finding the data, since vehicles with a common interest are supposed to meet more frequently as compared to vehicles with different interests. Hence, this will lead to reducing the number of hops. In another word, vehicles will be able to request contents and to find easily the most suitable forwarder or producer among the neighbor community members.
In our proposal, we consider a hybrid VNDN-VSN network where vehicles use IEEE 802.11p. We assume also that each requester node in the network can track the location of the original data producer thanks to the Grid Location Service (GLS) [
A community is defined as a group of individuals from all different backgrounds, who probably have never met yet, but who have similar lifestyles, share similar interests, or visit similar places. In line with Vehicular Social Networks (VSN), the notion of social community has been also extended to vehicular environments. In such communities, common interests, geographical location, and mobility patterns can hold vehicles, RSUs, drivers, passengers, and pedestrians’ smart devices together in the virtual community of vehicles [
In our work, we suppose that all social communities are constructed beforehand by a trusted entity (i.e., certification authority CA). A CA is also responsible for (i) creating, (ii) certifying public keys as well as (iii) signing or validating anything that has a relation with the social communities (
Due to VNDN’s fundamental properties, global information about nodes is often impossible to gather in practice. There is also the phenomenon of overlapping communities, that is, some nodes are members of not only one but two communities. As a result, using a typical community recognition method to determine VNDN’s community is problematic. We employ a local community detection approach based on core nodes in this study. The algorithm’s steps are as follows:
Step 1: Determine the node’s center value.
The center value of node
Step 2: Identify the seed nodes.
Step 1 is used to determine the center value of all nodes, and the nodes with the highest center value are chosen as prospective seed nodes. We eliminate certain nodes according to the algorithm below to avoid the selected nodes being in the same community:
Step 3: Expand the community.
The fitness function of a given community S is defined as:
The total of the internal node degree in S is
The fitness function for S for a given node
Step 4: Combine communities that are redundant.
The degree of overlap between communities is calculated as follows:
The fifth and final step consists of: Continue using Steps 3 and 4 until all nodes have been assigned to the communities.
A vehicle can belong to different communities at the same time. Moreover, each community is ranked from one to four (one being the highest priority). This ranking mechanism can be done according to the vehicle’s interest in the content produced and shared by community members (the degree of interest can be extracted from the user’s profile). We suggest using a priority-based cache replacement technique to delete the oldest content when the reserved cache space of communities is full. This way we can release space for the incoming communities’ contents.
In order to leverage the NDN name-based routing principles, we extend its hierarchical naming scheme to efficiently address VSN applications, end-users of a particular VSN, and the contents produced by end-users.
Fields | Description |
---|---|
VSNapp | Denotes a specific VSN application. |
UserID | Identifies an end-user in the VSN application. |
CenterOFInterest | Refers to the coordinates of a specific zone called, zone of interest. It enables each vehicle requester to describe the zone that it expects to visit. |
CommunityName | Determines the name of the community that produces the required content. |
ContentHierarchical |
Refers to the content produced by the end-user and its instances. |
In ETSI architecture [
A CAM may also include:
Interest One basic container that provides basic ITS-S-related data. One high-frequency container that provides highly dynamic data of the ITS-S. i.e., vehicle movement status and basic vehicle sensor data. One low-frequency container that provides static and non highly dynamic data of the ITS-S. One or more vehicle container that provides information specific to the vehicle role of the sender ITS-S, like roadwork vehicle, emergency vehicle, public transport vehicle, etc.
To adapt CAM messages with community awareness, we attempt to define a new low-frequency LF container composed of the following two optional containers as in figure Interest community list: provides the IDs of all the communities that the vehicle belongs to. Active communities: provides the current active community, in other words, the community that the vehicle is currently interested in its produced content.
Each receiver of the CAM message will extract its nearby vehicles memberships and store them in the neighbors’ table grouped by communities names to use them in the future forwarding processes (
Community name | Id | Position | Speed (Km/h) | direction |
---|---|---|---|---|
Taxi community | Vehicle A | (0.568, 1.556) | 66 | East |
Vehicle E | (0.695, 2.694) | 80 | East | |
Vehicle F | (3.452, 1.068) | 75 | West | |
Sport community | Vehicle B | (0.265, 0.9563) | 50 | East |
Vehicle C | (1.268, 4.569) | 75 | West |
The design of the basic VNDN requires that each vehicle in the network hosts three main data structures FIB, PIT, and CS. In our approach, we retain the same PIT behavior as proposed in the original NDN. Same thing for FIB; however, we have decided to divide the CS into two equal parts: one-half for community contents and the other half for general data. Therefore, as illustrated in
Social contents accessibility: We have two categories of contents: (i) ‘Public contents’ accessible by all vehicles in the network and (ii) ‘Private contents’ accessible only by community members (encrypted contents). One vehicle can request any data content by indicating its name in the interest packet, but if the requested data is private, only community members (CM) can decrypt it using their community IDs. The other vehicles that are unable to decrypt the data can simply discard the data packet or ask to join the community. Interest and data packet forwarding process: Two different cases can be recognized in our proposed data retrieval approach since we have two categories of contents (i.e., public and private): (i) the first case is when the vehicle requests for general VSN contents, (ii) the second case is when the vehicle requests for VSN community contents.
The forwarding action taken by the requester vehicle consists of generating an interest packet and then finding the most suitable forwarder following the same VNDN forwarding process as shown in
In the second case (see
We have proposed a distributed opportunistic caching model based on community concept. Under which each node makes its own decision according to its social memberships and its preferences to the distinct received contents. To do so, we have divided the CS into two parts, one for community contents and the second one for general contents. When the communities’ allocated cache space is full, a priority-based cache replacement approach is suggested to replace old contents. To accomplish this, we use
In this section, simulation parameters, scenarios, and performance metrics are described. In addition, extensive experiments are conducted to evaluate the performance of our proposed approach SAFT-VNDN and compare it with the basic VNDN implementation. Finally, the obtained results are discussed.
Our purpose is to analyze and compare SAFT-VNDN’s performance in urban scenarios under different network densities. To accomplish this, our simulations were performed using a customized event-driven network simulator (Ns2.34). we have integrated the three main data structures of the NDN nodes (CS, PIT, FIB) into the local memory of the created Agents. Furthermore, to handle the processing of the exchanged packets in the network. We have implemented the interest and data packets forwarding functions. We consider that all vehicles are equipped with the IEEE 802.11p standard for V2V communication, and have a communication range of 300 meters. To demonstrate the feasibility of SAFT-VNDN we applied a Manhattan mobility model in a 1000 m grid with 5 × 5 urban roads. We varied the density of vehicles between 50 and 250 vehicles per simulation area, with a maximum speed up to 50 km/h. To investigate the vehicles’ social characteristics and properties, we set up the simulation to have three distinct communities. The membership density is respectively 60%, 50%, 35% of the global network density. In addition, we assigned three different contents to each of the created communities, each produced by three different community producers. All community members are capable of generating and receiving community interest/data packets based on their interests and their community memberships.
Parameter | Default value |
---|---|
Density of vehicles | [50,250] |
Simulated area | 1 × 1 km |
Vehicles’ maximum speed | 70 (Km/h) |
Number of content requester | [50,250] |
Number of community content producer | 9 |
Communication technology | IEEE 802.11p |
Transmission range | 300 m |
The activity of the community members that varies over time determines the frequency of interest packet generation. However, for general contents, we assigned four distinct producers to four different contents and allowed all network vehicles to request them based on their interests. For cache management, as we previously discussed, we divided the CS into two parts, 50% of the storage space for community contents and 50% for general contents as well. For the sake of simplicity, we did not use
Instead, we opted for the FIFO method for cache replacement. For the same reason, we did not consider the priority of communities. In addition, simulations are run 25 times to reach 90% confidence. Results are discussed in terms of the following criteria:
Average data delivery delay: The average time between the times an interest packet is issued and a data packet is successfully delivered (including buffering, propagation, transmission, and retransmission delays). Data delivery ratio: the ratio of successfully delivered data packets to the total number of originally issued interest packets in a given period. Cache hit ratio: the ratio of cache hits to the sum between cache hits and cache misses.
Results in figure
This experiment finding is important to correctly interpret the results illustrated in
Simulation results in
Similar results were obtained with the second community that reached 100%. However, the DDR of the third community ranges from 61% to 100% because of the presence of collisions. While the DDR of general content drops to 60% because of the same reason. From these results, it is clear that our solution delivers significantly better performance than the native VNDN due to the selection of appropriate forwarders through their memberships, and the availability of CM data mules at less than three hops far from the requester as confirmed in
In this paper, we investigated the issues related to content dissemination and caching in VSNs, by proposing a novel hybrid VNDN-VSN forwarding technique based on social communities. Our forwarding technique mainly takes advantage of VSN that emerges as an ideal network to serve vehicles with common interests and allow them to exchange numerous types of content on a large scale. In addition, the inheritance of VNDN paradigm can optimize content dissemination by focusing on delivering contents regardless of their IP addresses and making them independent of where they will be forwarded. Moreover, the deployment of in-network caching can be a quite helpful solution to overcome vehicles’ high mobility issues. The simulation results revealed that our forwarding technique could achieve a favorable performance compared with traditional VNDN. For future work, we plan to consider further social aspects considering also drivers’ profiles in Online Social Networks to predict their future interests and hence, better optimize the search/forward processes.