Keywords

1 Introduction

As the world is changing day by day, people are focusing more and more on Internet of Things (IoT). Today, we see everything is getting smarter. We see smart homes, smart cities [1], and many smart devices which make things easier and smarter. Vehicles are also getting smarter day by day with the help of Internet of Vehicles (IoV) [2]. IoV vehicles are equipped with sensors and other technologies which help connect with other vehicles to transfer or receive data. We see many accidents occurring on roads due to a lack of concentration of drivers; the cars get off the roads and crash. If a vehicle has an early warning system, these types of crashes can be avoided. There could be a situation in which one vehicle is stuck in traffic. In such cases with the assistance of V2V communication, the vehicle which is stuck in traffic can inform other vehicles to go for a different route as in Fig. 1.

Fig. 1
An illustration of the S l o V concept. It depicts four road tracks. The first two road tracks from the right depict returning and a truck connected to four other cars and the truck is connected to L T E and R S U. The next two tracks depict going and a truck connected to three other cars on the adjacent road and also connected to L T E and R S U.

SIoV

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From IoV, SIoV [1] came into the picture. In SIoV [3], the vehicles that are having common interests are socially connected with each other. In SIoV, vehicles connected with each other share information of common interest like vacant parking places, weather conditions, traffic information, etc. An instance of SIoV [4] considers a scenario where an ambulance carrying a patient is moving towards a hospital. Now while moving, it connects with other ambulances to know the traffic condition on the way to the hospital so if traffic is there the ambulance gets notified earlier and then it can change the route. SIoV does not just socialize or connect vehicles, it also connects the driver or passengers. Sensors are also attached to the vehicles which show information about the vehicles like whether the vehicle is working fine or not to the driver or the passengers. Our major contributions to this study are as follows: first, to provide a system which can effectively rescue an accidental vehicle. Second, a system which is multipurpose or can be used in many domains like ambulance, defence, school buses management, etc., and last, to get real-time traffic information and inform other connected vehicles.

The paper is further organized as Sect. 2 which is the literature review, and Sect. 3 is the methodology applied in the paper. Section 4 is the data analysis and results, and Sect. 5 is the conclusion.

2 Literature Review

SIoV is a new technology and the recent work done in this field was explored to identify the gap. It is given in Table 1.

Table 1 SIoV overview
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3 Methodology

It is studied that there are very few studies that have discussed SIoV concerning emergency situations. Since no system is available which can provide rescue in emergency situations, our basic idea is to develop an artificial environment to test such situations and develop a system using SIoV which can provide emergency support and can be useful in many domains like Defence, Ambulance, and school buses. To develop an artificial environment, we have used SUMO and OMNet++ and we have analysed data collected from OMNet++. Also, Python script has been used to filter the data and generate graphs for further data analysis. Fig. 2 also gives a flow of the methodology.

Fig. 2
A flowchart. It depicts S U M O leading to create SUMO simulation using open street map leading to setting up Om Net plus plus, import INET, import VEINS, leads to from Veins import, to create a scenario using O S M files, and others.

Flowchart for this work

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3.1 Sumo Simulation Details

For making connectivity between school buses and vehicles, Simulation of Urban Mobility (SUMO) is used. SUMO permits displaying of intermodal traffic frameworks including street vehicles, public vehicles, and people on foot. In this, we have created a real-time traffic scenario. For the network scenario, we have to use SUMO open street map (OSM) feature to create a real-time network scenario of Noida Sec 125 near Amity University Noida.

Setting up OMNet++: We imported the SUMO OSM file in OMNet++ to display a real-life situation. For making a V2V interface simulation and for a better picture of the network, Veins is used which is an open-source network simulation framework for running vehicular-based projects which can offer unrestricted extensibility. OMNeT++, an extensible modular and compound-based C++ simulation library and framework for building network simulators, supports a variety of frameworks such as INET and Veins for vehicle-to-vehicle communication (V2V). It supports TCP/UDP protocol.

3.2 Data Extraction

Below here, it can be seen that in OMNeT++ simulation, the map that we’ve fetched from SUMO is working perfectly fine and vehicles are moving on roads. The portion below the map shows the data like which vehicle is moving in which lane, etc. Time is also previously set for vehicles moving on the road. As we can see in the data shown below the map, the vehicles move every 124 ms.

Datasets. The dataset below shows us the following details as explained in Table 2.

Table 2 Dataset parameters
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The formula for the same is given below

$$\text{SNIR}=\text{Signal Power/(Noise}\,+\,\text{Interference power})$$
(1)

3.3 Data Filtration

Data filtration is done to remove the multiple fields and any null values in the dataset that weren’t necessary for the study like CO2 emissions, etc. For this, a Python script is created to filter out the data and a code is written and implemented based on Python inbuilt libraries to create various graphs using Python script.

4 Data Analysis and Results

Data analysis is used to extract meaningful information from data and make decisions based on that knowledge. Analysing our past or future and making judgments based on it is what this is all about. After the deployment of the network over the simulation tool, we could establish a socially connected network where in case of an accident the nearby accessible active nodes can provide aid. So, during simulation we observed that nodes 68 and 70 had an accident. So, they reached out for help to other nodes in the network. We can see from the simulation that an accident took place between node 70 and node 67. Now if we see below that, node 70 is sending a signal to all other nodes that are there within seconds. Similarly, if two accidents occur then the nodes are capable to send a signal to other nodes or vehicles simultaneously.

After doing the analysis of nodes, we’ve created a graph which shows the SNIR packet loss for different nodes for node 70 and node 68 as in Figs. 3 and 4, respectively. The broadcast messages for node 68 and node 70 are shown in Figs. 5 and 6, respectively. These figures show a graph for node 68 and node 70 SNIR lost packet for different datasets like 130 nodes, 145 nodes, 160 nodes, 180 nodes, and 200 nodes.

Fig. 3
A box plot depicts Node 70 S N I R packet loss. The 145 node, 160 node, 180 node, and 200 node depicts a median at approximately 39 and the minimum depicts a value of approximately 5.

Node 70 SNIR packet loss

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Fig. 4
A box plot depicts Node 68 S N I R packet loss. The 145 node, 160 node, 180 node, and 200 node depicts a median at approximately 39 and minimum depicts a value of approximately 5.

Node 68 SNIR packet loss

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Fig. 5
A box plot depicts Node 68 broadcast receiver. The 130 node, 145 node, 160 node, 180 node and 200 node depicts a median at approximately 5 and the maximum for 180 node depicts a value of 12.

Node 68 broadcast receiver

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Fig. 6
A box plot depicts Node 70 broadcast receiver. The 160 node, 180 node, and 200 node depict a median at approximately 5 and the maximum for 145 node depicts a value of 12.

Node70 broadcast receiver

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Broadcast devices are simple messages from other apps or the system itself. These signals are referred to as events or intents on occasion. For example, applications can send out broadcasts to inform other apps that data has been downloaded to the device and is ready for them to use; the broadcast receiver will intercept this communication and take the required action.

The aim was to build a simulation to manage the connectivity between school buses in OMNet++. In the simulation, we could see that the vehicles are moving on roads and are perfectly visible with the time set at 113 ms.

5 Conclusion

In this study, a simulation has been developed by using OMNeT++, Veins, and INET. In this work, we varied the number of vehicles and did 5 simulations as 130, 145, 160, 180, and 200 vehicles or nodes to create connections and make them communicate with each other. As shown above, when the first node meets with an accident, it sends a packet named accident first to the radio manager and then sends it to the nearby other two nodes. In the paper, there is a brief explanation of the concepts used to achieve our goal. In the paper, we were able to gather datasets for 4 different node network for better results. Those numbers are 130, 145, 160, 180, and 200. In this paper, we showed communication between the vehicles. This is very helpful and if used in the real world, it could solve many problems like the problem of a school bus when stuck somewhere or it could be very helpful in emergency vehicles. The solution is not just helpful for school buses but is also helpful for emergency vehicles. We see many deaths in ambulances that take place daily in India because of not reaching on time, so with this, the vehicles could reach on time and lives can be saved.