Internet of Vehicles (IoV), a rapidly growing technology for efficient vehicular communication and it is shifting the trend of traditional Vehicular Ad Hoc Networking (VANET) towards itself. The centralized management of IoV endorses its uniqueness and suitability for the Intelligent Transportation System (ITS) safety applications. Named Data Networking (NDN) is an emerging internet paradigm that fulfills most of the expectations of IoV. Limitations of the current IP internet architecture are the main motivation behind NDN. Software-Defined Networking (SDN) is another emerging networking paradigm of technology that is highly capable of efficient management of overall networks and transforming complex networking architectures into simple and manageable ones. The combination of the SDN controller, NDN, and IoV can be revolutionary in the overall performance of the network. Broadcast storm, due to the broadcasting nature of NDN, is a critical issue in NDN based on IoV. High speed and rapidly changing topology of vehicles in IoV creates disconnected link problem and add unnecessary transmission delay. In order to cop-up with the above-discussed problems, we proposed an efficient SDN-enabled forwarding mechanism in NDN-based IoV, which supports the mobility of the vehicle and explores the cellular network for the low latency control messages. In IoV environment, the concept of Edge Controller (EC) to maintain and manage the in-time and real-time vehicular topology is being introduced. A mathematical estimation model is also proposed in our work that assists the centralized EC and SDN to find not only the shortest and best path but also the most reliable and durable path. The naming scheme and in-network caching property of the NDN nodes reduce the delay. We used ndnSIM and NS-3 for the simulation experiment along with SUMO for the environment generation. The results of NDSDoV illustrate significant performance in terms of availability with limited routing overhead, minimized delay, retransmissions, and increased packet satisfaction ratio. Besides, we explored the properties of EC that contribute mainly in path failure minimization in the network.
We have seen modernization at an extensive level in smart devices that we used to access network applications and services, with the increased numbers and types of new technologies over the past two decades. However, with the passage of time, the requirement for devices using the network and people is now stretching its limits. Software defined networking (SDN) and network function virtualization (NFV) provide new and unique ways to manage and design the network, while exploring the centralized control plane and programmable behavior of SDN offers a platform to implement and test the new innovative ideas. To provide a centralized view of the distributed network, the SDN separates the data plane from the control plane. Named data networking (NDN) is an emerging name-based content-oriented internet architecture built on the limitations of current internet protocol (IP) architecture. The IP address layer in the IP architecture was replaced with the content names. A comparison of the NDN and IP hourglass models is shown in
Technological advancements in the fields of computing, sensing, communication, automation, and networking technologies for vehicles are rerouting the trend toward internet of vehicles (IoV) instead of traditional vehicular ad-hoc networks (VANETs) [
To achieve an optimized and efficient routing methodology, SDN is an emerging technology that modernizes the IoV architecture. A lot of research papers have already discussed the architecture of SDN-enabled IoV. Increased traffic and number of vehicles, along with exceeding ratios of accidents, make the management of distributed traffic in big cities a hard task. SDN offers a logically centralized environment for IoV through heterogeneous networks [
The proposed protocol in our work uses a road aware strategy that takes care of the road segments created by the gateway nodes (nodes near intersections and road side units (RSUs)) and is very suitable for the IoV. Our approach to using the segment ID instead of the vehicle ID for path selection makes it more reliable and durable. Different technologies are utilized in forwarding the data packets, as the very packets from the vehicles on the road and SDN will be forwarded and received through the cellular network. The use of RSUs forwards the data packet to mobile as well as fixed destinations. As we know, the control message requires low bandwidth and low latency; therefore, we used a cellular network for this. The characteristics of the long-range coverage of cellular networks can benefit the transmission of emergency packets with few hops [
The remainder of the paper is organized as follow. We present the background and motivation in Section 2. Section 3 presents the main proposed scheme. Section 4 presents the simulation environment and graphs, along with the conclusion in Section 5.
In this section, we discuss the background of our proposed work, along with the motivation behind our research work and the problem statement that drove us to contribute here.
NDN is an emerging internet paradigm that shifts from the communication model of IP and focuses on “what” the content is rather than “where” the content is stored. Application-level names of the content are directly used for content retrieval. Names in the NDN model are user-friendly and hierarchically structured with a variable number of components such as URLs [
Two types of packets are used in connectionless and receiver-driven communication in NDN—interest packet and data packet. The pervasive in-network caching of the NDN is suitable for vehicular information [
At each node, three data structures are involved in the interest and data packet processing. Content store (CS) stores the names of the contents and incoming data, pending information table (PIT) stores the entries of un-entertained requests, and it will keep the record of the incoming face of the forwarded requests. Forwarding information base (FIB) is used to set up a routing table to select the outgoing interface if any potential forwarder exists. The term interface is used to indicate the incoming and outgoing interfaces in NDN [
On the interest packet reception, a node will check its CS for data matching first. In case it finds the matching entry in CS, then it will forward the cached data back to the consumer; otherwise, it will check the PIT for whether the same request is forwarded or not. If it finds the matching entry, it will discard the interest packet; otherwise, PIT will update after FIB lookup and returns to the outgoing interface. Then the interest packet will forward toward the destination using the potential forwarder. Data packets are not routed; instead, they simply follow the breadcrumbs of interest packets. To serve future requests, each intermediate node may cache data packets. An NDN forwarding model is shown in
Mobility refers to the relative maximum speed at which one can achieve reliability. Mobility, migration state, and handover of vehicles are studied in different research works. The handover mechanism is very challenging in an SDN-based IoV environment compared with the traditional cellular network. There is a need for the renegotiation of radio resources with a new SDN controller. Random changing topology needs to update the flow table in the SDN controller. As the use of an EC controller encourages service redirection and live migration that increases the handover complexity, the generation of handover will be simultaneous for the multi-hop links and the edge controller services. In near future, the position of vehicles can be estimated using the trajectory prediction component by using the direction, velocity, position, and destination of the vehicle. It will help with updating the flow table in SDN and service migration. Handover mechanism failure is another issue in which the recovery of failure and error handling should be considered carefully, and there should be minimum propagation delay of the warning message.
Vehicular networks are unpredictable with high mobile environments and random changing topologies [
In [
A novel adaptive routing and switching scheme (NARSS) in [
In [
In this section, we discuss our proposed routing mechanism NDSDoV, which uses SDN and EC for the overall management of the network. The data structure of SDN consists of a global information table (GIT) and routing information table (RIT). GIT has the information of all the vehicles received from the EC and maintains the table with the information of vehicle ID, segment ID, speed, direction, content names, and position in a separate column against each vehicle ID. RIT maintains the best routing path after calculating the path estimation. Local information table (LIT) in EC has the information of all the vehicles received from each vehicle and maintains the table with the information of vehicle ID, segment ID, speed, direction content names, and position in a separate column against each vehicle ID. In our forwarding mechanism, we describe the complete process in which packets will be forwarded from the consumer node toward the producer node using the best path calculated by the SDN controller. Four types of packets are used in this proposed strategy: NDSDoV_Int_Pkt, NDSDoV_Data_Pkt, L_Info_Pkt, and EC_Info_Pkt, as shown in
EC then forwards the information collected from the vehicles toward the SDN controller using EC_Info_Pkt, and the second level of routing starts from here. The SDN controller will maintain its table with the information received from the EC. The SDN controller can then estimate the shortest routing path using the information of the table. SDN computes the shortest path using the minimum hop count, relative velocity, and direction. After computing the shortest path, the SDN stores the path in ascending order. Selection of shortest path depends only on the roads with the vehicle’s density
A hard timeout is set in every vehicle against every rule made by the controller. The entry is removed from the table whenever a vehicle goes out of the range or after a timeout. A source vehicle sends the data in unicast manners toward the destination until the path expires. A new path is computed by the SDN in case of the failure to complete the transfer of data. For this purpose, the SDN is being notified to recover the path. A valid flow entry in the flow table is investigated by the vehicle before sending the packet towards the destination. If there is no matching entry in the flow table, then the request is sent to the EC, and after checking its table, EC will then forward the query toward the controller. The controller updates the shortest path in the data plane toward the destination based upon information from the road segments and the vehicles on them. Vehicles along the path and RSUs receive the updated flow rule from the SDN controller. Source vehicle updates the controller to recompute the flow entries after sending failure messages in case of a change in topology when a neighbor node ran out of it. Whenever the SDN controller receives the failure notification, the shortest path will be recomputed, and a newly computed path is then updated in vehicles. The entry of a vehicle will be removed from the table after waiting for the soft-mount whenever it leaves the road segment without any data forwarding process. The value will remain there in the table if the value of a hard time is greater than the value of soft timer. Otherwise, the value of a hard time should be zero. There is another scenario in which a vehicle leaves the road segment with a transmission going on at the same time, then the SDN controller selects an updated topology path to further the transmission.
Let us assume that |
All the vehicle nodes broadcast their |
Upon receiving the NDSDoV_Int_Pkt at vehicle |
Checks content names _CS (\,) |
If (content ID found) |
Forward Data back to consumer (\,) |
Else |
If (entry found) |
Discard the |
Else forward |
SDN selects the path towards the producer |
Find Nbr_vehicle () and Send packet to Nbr_vehicle (\,) |
Else If (selected path contains |
Find Nbr_vehicle to |
Send packet to GW_V (\,) |
Else |
Find Nbr_vehicle to |
Send packet to |
After computation of mathematical model and Data |
End if |
End if |
End if |
In our proposed scheme, the vehicle will forward a path failure notification toward the EC on the road segment, either in case of path flow entry removal or due to the topology change. For each road segment, the SDN controller computes the various best and shortest paths under its vicinity. The SDN controller will first check the type of failure whenever it receives a failure notification from the EC. If the EC receives a failure notification from inside the road segment, then it will check its table for the shortest path. In the case of path failure outside the road segment, there is always a route request forwarded to the SDN. EC can have various path failure notifications from various vehicles; in this case, the path failure request against the same path ID will be discarded by the EC.
It was observed that the vehicular network had a static network for a particular interval of time. However, up to a certain time, the change in topology can be predicted based on the mobility model. There are multiple communication links are available and possible between the source and the destination, and the path estimation for all of those paths is not always reasonable. Therefore, we focused on the on-demand routing strategy in terms of path estimation. Thus, the significance and table principle for path estimation is an exploration of average path intervals in our scheme. For our proposed scheme, we introduced a new probabilistic path estimation model. The exploration of the most durable and stable paths based on various parameters is the main remarkable property of our protocol. The parameters incorporated in our proposed scheme are the number of hops, link connectivity, direction, and velocity. Incorporating discrete parameters, the SDN controller determines the duration of each path to provide reliable links. Every vehicle perceives its velocity and its neighbors’ positions from the beacon. This information can be used to compute the connection time span of the two neighboring vehicles having valid communication ranges with each other.
In this section, our aim is to deduce an expression for the duration of the path between two vehicles by deriving and calculating mathematical relations such as the number of hops and link duration. In our estimation model, a traditional traffic flow principle was used for data forwarding purposes to represent the vehicular environment efficiently. The vehicles were assumed to follow Poisson distribution to obtain the probability distribution function. The notations used are defined in
Variable | Description |
---|---|
C | Consumer node |
P | Producer node |
Relay node | |
Consumer and producer node distance | |
Range of consumer vehicle | |
Distance from producer node to range of consumer node | |
Intersection area 1 | |
Intersection area 2 | |
Total area | |
Sub segment area of road | |
Distance from consumer node to rely node | |
Velocity of consumer node | |
Relay node’s velocity | |
Total number of expected hops | |
Probability density function of relative velocity | |
A | Consumer to producer angle between 2 lines |
Λ | Constant |
To find a more stable and durable path between the consumer and the producer, a communication link with the minimum number of hops is required. As the closest node with the producer will cover the maximum distance and minimum hops. Hence, the farthest node from the consumer and closest node to the producer will be selected to forward the packet. The very purpose directs the need to find the area of the intersection of circles to find the next hop with the maximum communication range. We can find the area of the region using the standard formula in
We can calculate the total area of both segments by
However,
and
We can use
Estimation of the path depends significantly on the speed and direction of the vehicle. Link duration is directly affected by the direction of the vehicle. The mathematical model for path estimation proposed in our work was also used in [
In scenario 1, when both vehicles have the same velocity and direction then
We can represent
We can use the above formula with the positive sign if the direction is opposite for both of the vehicles, and minus sign can be used for the same direction with velocity
To find the number of hops between consumer and producer nodes, it is essential to find the average number of neighbor nodes between them. Within the transmission range, the Poisson distribution model is used for the available vehicles on the road. The probability of finding the producer node and next hop is the same. The distance from the consumer to the first next hop can be calculated using
The most important estimation is the link duration estimation of every vehicle that is significant for finding the best route having durable and maximum duration. As we know, time = distance/speed
In
Residual link life would be
We have to find the distance beyond which the vehicle moves out of the communication range that is
One of the fundamental design parameters in VANET is complete path estimation. The probability density function of path duration can be determined by considering the remaining link life of the hops.
We can compute the average path duration using
In this section, we discuss the working of the proposed EC in our scheme for the mobility issue. Link breakage due to the topology change is a critical issue in VANETs, which affects the overall performance of the network very badly. The focus of this section is to overcome the issue of mobility. In our proposed research model, the main work of EC is to collect the information from every vehicle to know the exact topology in real-time. Vehicles periodically send their information to the EC, including direction, road ID, position, and speed. EC checks the vehicle details in its table after receiving the information from the vehicle. In case of a legitimate change in the position of the vehicle, it will update the SDN controller with the latest information; otherwise, EC will update its database on new vehicle entry. This approach to updating the SDN results in fewer network overheads. Vehicles provide information to the EC at a regular interval of time that is 0.2 s, which is calculated after many simulations. This time can be changed according to the type of traffic and road conditions. More frequent information will be required in the case of high-speed movement of the vehicles. There are two different scenarios to send the L_Info_Pkt and EC_Info_Pkt to the SDN controller: (a) a packet can be sent to the SDN after a fixed interval of time, (b) a packet can be sent after a specific change in the position of the vehicle, that is, 20–30 m.
Calculation of the hello interval is performed using
In this section, different simulation parameters and matrices are used to evaluate the performance of our proposed protocol NDSDoV. We compared NDSDoV with native BSMS and BSAM protocols. We discuss the results graphs to show the clear difference in the performance of our protocol.
In our proposed mechanism, we used ndnSIM [
The transmission range of each node was 250 m. The network range in our simulation was 5000 m2. We used variable topology density over different numbers of sender/receiver pairs of vehicles in the IoV scenario to have clear results. The percentage of senders or consumer vehicles was 70%. The percentage of producer vehicles was different (
Three different performance matrices were used to evaluate the results of the NDSDoV model.
(a) Total number of retransmissions: This defines the total number of unsuccessful data packets retransmitted again by the sender nodes due to any network issue. (b) End-to-end delay: This defines the total average delay from the initiation of the Interest packet until the completion of the transmission process. (c) Interest packet satisfaction rate: This defines the total number of packets successfully delivered.
We evaluated the performance of NDSDoV and comparative schemes against the average speed of the vehicle, simulation time, and a number of producer pairs.
Parameters | Values |
---|---|
Number of vehicles | 20–100 |
Type of vehicle | Mobile |
Simulation area | 5000 ∗ 5000 m |
Total simulation time | 1000 s |
Range of transmission | 250 m |
Minimum vehicle speed | 0–4 m/s |
Maximum vehicle speed | 5–25 m/s |
Percentage of consumer vehicles | 70% |
Percentage of producer vehicles | 5%, 10%, 15%, 20% and 25% |
Mobility model | Random walk |
Traffic pattern | CBR |
Communication technology | IEEE 802.11 OCB, Cellular 3G/LTE |
Traffic generator | SUMO |
Communication type | V2V, V2I |
Tx power | 1.6 MW |
Simulation runs | 10 |
In
In
We evaluated the comparative schemes BMAS and BSMS with our proposed NDSDoV against the packet satisfaction ratio in
In this paper, a novel and efficient forwarding mechanism, NDSDoV, has been introduced that supports the mobility of the vehicles in addition to exploring the cellular network for the control message to and from the SDN controller and EC with low latency. Our proposed mechanism also mitigates the broadcast storm issue. In the IoV environment, we introduced the concept of EC to maintain and manage in-time and real-time vehicular topology. In our work, we also proposed a mathematical estimation model that assists centralized EC and SDN in finding the most reliable and durable path. The simulation graphs and results of NDSDoV outperformed other schemes in terms of availability with limited routing overhead, packet retransmissions, minimized delay, and increased packet satisfaction ratio.
This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the National Innovation Cluster R&D program (R&D, P0015131).