Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management
Abstract
:1. Introduction
- In terms of various collision prevention methods, such as packet delivery rate, latency, failure rate, and real-time traffic throughput, the authors analysed the DNN-Internet of Things (IoT)-BA model with existing deep learning models;
- The authors devised and updated the routing table to reduce vehicle collision rates in real-time based on the traffic information collected by the IoT agents across the network;
2. Network Model
- Vehicle Unit: VANETs are responsible for communication with nearby vehicles along the highway sector or with the RSUs at the edges/corners in simple terms for transport purposes. VANETs are also responsible for facilitating transport. We used elliptical curve encryption as an encryption algorithm in this paper to generate cryptographic credential information and store it in the vehicle. The Global Positioning System (GPS) is responsible for locating the vehicle inside. In a road segment, RSUs can determine the total number of vehicles using a GPS unit [40,41,42,43,44,73,74,75,76,77];
- Roadside units (RSUs): The RSU is an access point on the roads, where details of vehicle units can be found along the roads. The vehicle units’ encrypted street segments are regarded as the relay for the Transport Message Channel. An IoT-BA is connected to RSU by a faster means of communication. The RSU operates the cryptographic credentials to decode vehicle information and the DNN-IoT-BA algorithm and stores them;
- Traffic Management Center (TMC): The IoT-BA calculates the traffic density using road segments. In order to obtain road traffic information, the directional connection of TMC to RSUs and other IoT-BAs are used. In order to avoid congestion in road segments, the IoT-BA transfers the collected information to the DNN. The differential between TMC and IoT-BA is that the former is a fixed segment and the latter is a network-wide mobile segment.
- Detection range is defined as the communication region between the receiver sensitivity threshold of any two vehicles and the SINR, which are required for payload;
- Data Exchange range is defined as the communication region in which the data transmission takes place;
- Time before handover is defined as the communication region where the OBU prepares for handover;
- Time to handover is defined as the communication region where the actual handover takes place.
- λ is the wavelength;
- max(p) is the maximum level of transmission power;
- α is defined as the minimum level of a path loss coefficient.
- V is the velocity of the VU;
- P is defined as the Perimeter of an RSU;
- A is defined as the area of an RSU.
3. Traffic Management Model
3.1. Mobile Agent Unit
3.2. Infrastructure Unit
3.3. Bat Algorithm (BA)
- fi is the frequency;
- is the velocity;
- is the position;
- is the loudness;
- is the emission pulse rate;
- 𝑥∗ is the global best;
- Fmin is the minimum frequency;
- fmax is the maximum frequency.
- ε is the arbitrary vector;
- At is the normal commotion at step t.
- rand is considered a random vector with uniform distribution.
- 0 < α < 1 and γ > 0
- ai is the actual value;
- di is the estimated value.
3.4. Infrastructure Unit Workflow
4. Performance Evaluation
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameters | Value |
---|---|
Total vehicle units | 100–300 |
Channel carrier frequency | 5.9 GHz |
Packet length | Uniform |
Maximum transmission | 20 mW |
Simulation area | 1500 m × 1500 m |
Vehicle velocity | 20–50 kph |
Bit rate | 18 Mbps |
Signal attenuation threshold | −90 dBm |
Path loss coefficient | 2 |
Transmission range | 500 m |
MAC protocol | 802.11 p |
Traffic type | CBR |
Beacon interval | 0.5 s |
Data rate of MAC | 6 Mb |
Mobility model | Krauß model |
CBR rate | 4 packets/sec |
Simulation time | 1000 s |
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Kannan, S.; Dhiman, G.; Natarajan, Y.; Sharma, A.; Mohanty, S.N.; Soni, M.; Easwaran, U.; Ghorbani, H.; Asheralieva, A.; Gheisari, M. Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management. Electronics 2021, 10, 785. https://doi.org/10.3390/electronics10070785
Kannan S, Dhiman G, Natarajan Y, Sharma A, Mohanty SN, Soni M, Easwaran U, Ghorbani H, Asheralieva A, Gheisari M. Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management. Electronics. 2021; 10(7):785. https://doi.org/10.3390/electronics10070785
Chicago/Turabian StyleKannan, Srihari, Gaurav Dhiman, Yuvaraj Natarajan, Ashutosh Sharma, Sachi Nandan Mohanty, Mukesh Soni, Udayakumar Easwaran, Hamidreza Ghorbani, Alia Asheralieva, and Mehdi Gheisari. 2021. "Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management" Electronics 10, no. 7: 785. https://doi.org/10.3390/electronics10070785
APA StyleKannan, S., Dhiman, G., Natarajan, Y., Sharma, A., Mohanty, S. N., Soni, M., Easwaran, U., Ghorbani, H., Asheralieva, A., & Gheisari, M. (2021). Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management. Electronics, 10(7), 785. https://doi.org/10.3390/electronics10070785