# A Machine Learning-Aided Network Contention-Aware Link Lifetime- and Delay-Based Hybrid Routing Framework for Software-Defined Vehicular Networks

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## Abstract

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## 1. Introduction

- We present a novel hybrid routing algorithm for SDVNs that uses collected metadata from nodes to estimate parameters at the controller to compute routes that yield the least communication cost, the highest packet delivery ratio, the least latency, and moderate channel utilization, on average, compared to routing in VANETs (AODV) and SDVNs (Dijkstra);
- This research provides insight into the employment of machine learning for accurate and computationally efficient parameter computation at the controller, such as the estimation of link delay and lifetime, satisfying the communication requirements of vehicular networks;
- This research experimentally investigates the factors under which the proposed hybrid routing framework is enhanced, so that these factors can be effectively used by future researchers to boost the performance of the proposed novel hybrid routing framework;
- The proposed routing algorithm will be very useful for more efficient dissemination of information in the data plane of future SDVNs than existing approaches.

## 2. Background and Literature Review

#### 2.1. VANET

#### 2.2. SDN

#### 2.3. SDVN

#### 2.4. SDVN Architectures

#### 2.5. Routing

#### 2.6. Routing in SDN

#### 2.7. Routing in SDVN

#### 2.8. Machine Learning for Routing in SDVN

## 3. Proposed Methodology

#### 3.1. Overview of the Routing Framework

#### 3.2. Estimation of Link Lifetime

^{st}–8

^{th}-order terms for difference in x coordinates (${\delta}_{x}$), difference in y coordinates (${\delta}_{y}$), difference in x direction velocities ($\delta {v}_{x}$), difference in y direction velocities ($\delta {v}_{y}$), difference of accelerations in x direction ($\delta {a}_{x}$), difference of accelerations in y direction ($\delta {a}_{y}$), remaining distance to maximum transmission distance ($\delta D=\sqrt{{\left({D}_{ij}^{max}\right)}^{2}-\delta {x}^{2}-\delta {y}^{2}}$), product of x direction differential acceleration and velocity ($\delta {a}_{x}\delta {v}_{x}$), product of y direction differential acceleration and velocity ($\delta {a}_{y}\delta {v}_{y}$), product of x direction differential acceleration and displacement ($\delta {a}_{x}{\delta}_{x}$), product of y direction differential acceleration and displacement ($\delta {a}_{y}\delta y$), the product of x direction differential displacement and velocity ($\delta x\delta {v}_{x}$), and, lastly, the product of y direction differential displacement and velocity ($\delta y\delta {v}_{y}$) between the nodes i and j. The output of the neural network is the wireless link lifetime. The proposed DNN consists of two hidden layers of size 1024 per layer, as evident from Figure 5. By providing the preceding set of input features, the neural network is able to predict wireless link lifetime once it is fitted to real data, such that it can replace the optimization-based approach.

#### Computation of the Link Lifetime Matrix

#### 3.3. Estimation of Link Delay

#### 3.3.1. Investigating the Factors Affecting Collision Probability to Formulate an Average

#### 3.3.2. Computing Average Contention Delay per Channel

#### 3.3.3. Computing Normalized Network Contention

#### 3.3.4. Computing Average Per-Channel Delay at Each Hop

#### 3.3.5. Predicting Exact One-Hop Channel Delay Using Machine Learning

- Number of links in the same communication channel connected to the hop (${N}_{iWL}$ or ${N}_{iWI}$);
- Average pending transmission packet distribution factor of the node and its neighbors ($\overline{{Q}_{i,nei}}$), which can be calculated as given in Equation (34):

- Data packet size (${P}_{D}$), packet size of RTS (${P}_{rts}$), packet size of CTS (${P}_{cts}$), packet size of ACK (${P}_{ack}$), and data rate (DR) to compensate for transmission delay errors;
- Input Short Inter-Frame Space (SIFS) to compensate for processing delay errors;
- E, ${T}_{c}$, $C{W}_{min}$, ${T}_{slot}$, 1th–6th-order terms of $\overline{{\rho}_{WL}}$, and 1th–6th-order terms of $\overline{{\rho}_{WI}}$.

#### 3.3.6. Computation of the Link Delay Matrix

#### 3.4. Hybrid Algorithm for Finding the Highest Stable Least Delay Path or Highest Stable Shortest Path

#### 3.5. Proposed Flow Table Architecture

#### 3.6. Flow Table Update at the Controller

#### 3.7. Adaptive Flow Rule Computation, Update, and Installation

#### 3.8. Overall Routing Process

- We use the data collection optimization model proposed in our previous work [65] for metadata collection for the routing framework. Metadata refers to all data, namely, status data (node addresses, position, velocity, acceleration), node address set of all one-hop neighbors of each node (${\mathcal{S}}_{i}$), average pending transmission packet distribution factor of each node ($\overline{{\mathcal{Q}}_{i}}$), and average queue size of each node ($\overline{{\mathcal{R}}_{i}}$), which are required to compute the routes using the proposed hybrid routing algorithm given in Section 3.4. However, in the very first routing cycle, as routing has not taken place at least once, $\overline{{\mathcal{Q}}_{i}}$ and $\overline{{\mathcal{R}}_{i}}$ cannot be collected, and only status data and ${\mathcal{S}}_{i}$ are collected, as shown in block ”U0“ in Figure 13. These metadata are unicasted to the controller node from the agent nodes in the data collection optimization model. In the initial routing cycle, only $X[N,N,2]$ and $T[N,N,2]$ are computed at the controller node, as shown in block ”K0“ in Figure 13. $X[N,N,2]$ (the adjacency matrix of position) is computed using the position data of all nodes, while $T[N,N,2]$ is computed using Equation (5) with the aid of the wireless link lifetime prediction DNN by providing differential position, velocity, and acceleration to the machine learning model, as described in Section 3.2. In the very first routing cycle, $D[N,N,2]$ cannot be computed, as data do not exist for $\overline{{\mathcal{R}}_{i}}$ and $\overline{{\mathcal{Q}}_{i}}$. Furthermore, it is not necessary to compute other parameters that are required to compute normalized network contention ($\mathcal{C}$), as data do not exist to compute the average pending transmission packet distribution factor of the network ($\overline{\mathcal{Q}}$). Because of this, in the very first routing cycle, the value of $\mathcal{C}$ is assumed to be one, to utilize the highest stable least distance as the mode for the routing algorithm given in Section 3.4;
- In the initial cycle, once $X[N,N,2]$ and $T[N,N,2]$ are computed, routes are computed using the hybrid routing algorithm by choosing stable distance as the metric, and the output of this algorithm is fed to flow table update algorithm to update flow table entries. Routing algorithm can compute only routes from all other nodes to a given destination node, so routing algorithm and flow table update algorithm should be executed iteratively N times, where N is the total number of nodes, by varying the destination node ID, to find routes from each and every source node to each and every destination node, as shown in block “Z0” in Figure 13. Using the parent vector, path valid time vector, and destination node ID output from routing algorithm, flow table update algorithm updates flow table entries. This updating of the flow table by flow table update algorithm occurs at the end of each iteration of finding routes by routing algorithm. At the end of the functioning of block “Z0” (once the updating of the flow table is over), the controller must unicast FlowMod packets related to each flow entry of the flow table to corresponding switches, as shown in block “B1” of Figure 13;
- Routing occurs at a routing frequency (f), and, at each routing cycle, including the very first, routing occurs as packet transmissions are scheduled, as is evident from block “A” in Figure 13. Packets are forwarded through each node by inspecting the flow table of the node. Because of the implementation of route valid timestamp in the proposed flow table, when a flow table match occurs with source address not equal to destination address, action not explicitly defined to drop, and current timestamp greater than the route valid timestamp, a packet_in message will be generated and sent to the controller, as shown in block “C1” in Figure 13, as the current flow entry in the flow table at the switch has expired. Then, in response to the packet_in message, the adaptive flow rule computation, update, and installation algorithm will run as shown in block ”C2” in Figure 13 to create a FlowMod packet containing flow modification rules, which will be unicasted to the corresponding switch, as shown in block “C3” in Figure 13, to update the corresponding flow table entry in the switch with updated flow rules. Note that, if a flow table mismatch occurs due to a packet scheduled to a destination node that does not exist in the vehicular network, a packet_in message will be sent to the controller. Then, the adaptive flow rule computation and update algorithm will inspect the destination, and, as it does not exist in the topology database, a FlowMod packet to drop the packet at the switch will be sent back to the switch. During the entire routing time period (the time period during which routing occurs), each node monitors its transmission queue size at discrete time intervals and computes the parameters’ average queue size ($\overline{{\mathcal{R}}_{i}}$) and average pending transmission packet distribution factor ($\overline{{\mathcal{Q}}_{i}}$), as shown in block “K1” of Figure 13. Computed $\overline{{\mathcal{R}}_{i}}$ and $\overline{{\mathcal{Q}}_{i}}$ values are kept with the nodes until they are unicasted to the controller node in the next routing cycle, along with other metadata, as shown in block “U1” of Figure 13. The routing in all routing cycles is similar (Functioning blocks “K1”, “A”, ”C1”, “C2”, and “C3” operate in each routing cycle, including the initial cycle, similarly);
- Starting from the second cycle (subsequent cycle 1) onwards, agent nodes should collect and unicast status data, a set of node addresses of one-hop neighbors of each node (${\mathcal{S}}_{i}$), $\overline{{\mathcal{R}}_{i}}$, and $\overline{{\mathcal{Q}}_{i}}$ computed from the previous routing cycle, as evident from the “U1” block in Figure 13. Thus, at the controller node, $\overline{\mathcal{Q}}$ is computed using the $\overline{{\mathcal{Q}}_{i}}$ values received from all the nodes. $X[N,N,2]$ and $T[N,N,2]$ are computed at subsequent routing cycles in the same manner specified for the initial cycle. Furthermore, starting from subsequent cycle 1 onwards, normalized homogeneous link entropy with respect to wireless links (${H}_{WL}$), normalized homogeneous link entropy with respect to wired links (${H}_{WI}$), average collision probability for wireless links ($\overline{{\rho}_{WL}}$), average collision probability for wired links ($\overline{{\rho}_{WI}}$), and average contention delay for wired links ($\overline{{\rho}_{WI}}$) are computed, as shown in block “K2” of Figure 13. Using previously computed parameters, normalized network contention ($\mathcal{C}$) is computed using Equation (29). Furthermore, the adjacency matrix of link delay $\left(D\right[N,N,2\left]\right)$ can be found using Equation (35), with the help of the one-hop per-channel delay prediction DNN. Descriptions of the computation of the above parameters are described in Section 3.3;
- Once the link delay matrix $\left(D\right[N,N,2\left]\right)$ and normalized network contention ($\mathcal{C}$) are found, the routing algorithm will choose the mode as highest stable least distance or highest stable least delay by comparing $\mathcal{C}$ with ${\mathcal{C}}_{th}$. However, unlike the initial cycle, routes will be computed, and flow table entries will be updated using the adaptive flow rule computation and update algorithm only upon reception of packet_in messages. As was described in the explanation of that algorithm, each packet_in message may not result in computing and updating the flow table at the controller. If the flow table entry corresponding to the packet_in message is already updated, that entry will be sent to the corresponding switch to update its flow table using a FlowMod packet.

## 4. Results

#### 4.1. Performance Evaluation Metrics

#### 4.1.1. Root Mean Squared Error (RMSE)

#### 4.1.2. Average Computational Time (${T}_{com}$)

#### 4.1.3. Average Communication Cost ($\overline{C}$)

#### 4.1.4. Average Channel Utilization (${\overline{U}}_{j}$)

#### 4.1.5. Average End to End Latency ($\overline{\mathcal{T}}$)

#### 4.1.6. Average Packet Delivery Ratio ($\mathcal{R}$)

#### 4.2. Configuration of the Simulation Environment

#### 4.2.1. Configuration for Routing

#### 4.2.2. Configuration of Vehicular Mobility Scenarios

#### 4.2.3. Configuration of DSRC

#### 4.2.4. Configuration of LTE

#### 4.2.5. Configuration of RSUs

#### 4.2.6. Controller Node

#### 4.2.7. Data Packet

#### 4.2.8. Association of Communication Cost per Channel

#### 4.3. Performance of Wireless Link Lifetime Prediction and One-Hop Channel Delay Prediction Using DNNs

#### 4.4. Routing Performance Evaluation

#### 4.4.1. Impact of Link Lifetime Threshold

#### 4.4.2. Impact of Routing Frequency

#### 4.4.3. Impact of Network Size

#### 4.4.4. Impact of Vehicular Mobility

## 5. Discussion

- H1 —The communication cost of the proposed routing is higher than AODV;
- H2—The end-to-end latency of the proposed routing is higher than Dijkstra;
- H3—The PDR of the proposed routing is lower than AODV.

## 6. Conclusions and Future Research

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A. Metadata Collection for Routing Framework

## Appendix B. Sample Delay Calculations

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**Figure 1.**Pseudo-code for finding the highest stable shortest/least delay paths to a given destination node.

**Figure 3.**Approach to adaptively compute routes, update flow table, and create a FlowMod packet upon receiving of a packet_in message.

**Figure 6.**Illustration of delay occurrence sequences in CSMA–CA and CSMA–CD, (

**a**) Delay occurrence sequence with stop and wait with RTS–CTS flow control in CSMA–CA. (

**b**) Delay occurrence sequence without flow control in CSMA–CD.

**Figure 7.**Entropy calculation for two network instances. (

**a**) Vehicular network instance at time t − 1, (

**b**) Vehicular network instance at time t.

**Figure 8.**Network instances showing routing paths having different network per-channel link entropy, demonstrating different contention levels. (

**a**) Low-entropy network instance having low collision probability, (

**b**) High-entropy network instance having high collision probability.

**Figure 9.**Network instances having different PTPDF values, demonstrating different collision probabilities. (

**a**) Network with low PTPDF having low collision probability, (

**b**) Network with high PTPDF having high collision probability.

**Figure 10.**The structure of the deep neural network for predicting one-hop total delay per communication channel.

**Figure 12.**A sample routing path of the adjacency list ${P}_{8}\left[3\right]$, depicting node ID and link types.

**Figure 13.**The process of routing in each routing cycle using the proposed machine learning-aided network contention-aware link stability- and delay-based routing framework in SDVN.

**Figure 14.**Different mobility scenarios for generating mobility traces for the vehicular network. (

**a**) Urban mobility scenario in New York City, USA, (

**b**) Non-urban mobility scenario in Pelawatta, Sri Lanka, (

**c**) Autobahn mobility scenario in TCH expressway, Canada.

**Figure 15.**Training curves of DNNs and computational complexity comparison of wireless link lifetime prediction models: (

**a**) Training curves of DNNs, (

**b**) Computational complexity comparison of two approaches for wireless link lifetime prediction.

**Figure 16.**Performance evaluation of routing frameworks under various link lifetime threshold values. (

**a**) Communication cost fluctuation for various link lifetime thresholds, (

**b**) Channel utilization fluctuation for various link lifetime thresholds, (

**c**) Latency fluctuation for diverse link lifetime thresholds, (

**d**) Packet delivery ratio fluctuation for various link lifetime thresholds.

**Figure 17.**Performance evaluation of the routing frameworks under various routing frequencies. (

**a**) Communication cost fluctuation for diverse routing frequencies. (

**b**) Channel utilization fluctuation for various routing frequencies. (

**c**) Latency fluctuation for diverse routing frequencies. (

**d**) Packet delivery ratio fluctuation for diverse routing frequencies.

**Figure 18.**Routing performance evaluation under diverse vehicular network sizes. (

**a**) Communication cost fluctuation under various network sizes. (

**b**) Channel utilization fluctuation for diverse network sizes. (

**c**) Latency fluctuation for diverse network sizes. (

**d**) Packet delivery ratio fluctuation for diverse network sizes.

**Figure 19.**Routing performance evaluation under various mobility scenarios.(

**a**) Communication cost fluctuation for diverse mobility scenarios. (

**b**) Channel utilization fluctuation for diverse mobility scenarios. (

**c**) Latency fluctuation for diverse mobility scenarios. (

**d**) Packet delivery ratio fluctuation for various mobility scenarios.

Routing Framework | Routing Technique |
---|---|

Hierarchical SDVN [42] | Traditional VANET routing protocols with hierarchical controllers |

Resource scheduling scheme [43] | Greedy routing with objective of minimizing communication cost |

On-demand routing protocol [44] | Two-level packet forwarding using Bellman Ford algorithm and improved AODV |

Spray-and-pray multiple copy routing [45] | Graph-based least communication steps to minimize latency |

Globally optimized routing [46] | Minimum optimistic time-based shortest path routing algorithm |

Link stability-based routing [47] | Routing based on shortest stable path |

Cooperative data routing and scheduling [48,49] | Routing by prioritizing traffic type to minimize service latency while maximizing packet delivery ratio |

Tribrid routing protocol [50] | Stable shortest path routing satisfying QoS parameters |

Cognitive routing protocol [51] | Stable path routing with spectrum sensing |

Cluster-based routing (ICDRP-F-SDVN) [52] | Clustering algorithm with overhead reduction approach with backup VANET routing |

Highway routing [53] | Optimal routing path selection by predicting destination vehicle location using hidden Markov model |

Multipath routing [54] | QoS and flow rule space constrained routing using machine learning |

Trust-based routing [55] | Selects routing paths with highest trust using Deep Q learning |

Intelligent fuzzy-based routing [56] | Uses reinforcement learning to select most stable routing path |

Mobility adaptive routing [57] | Routing using Q-table updated by distributed Q learning |

Hierarchical routing [58] | Routing using optimum sequence of grids using Q learning |

Notation | Description |
---|---|

${D}_{ij}$, ${D}_{ij}^{max},\text{}{\delta}_{x},\text{}{\delta}_{y},\text{}\delta {v}_{x},\text{}\delta {v}_{y},\text{}\delta {a}_{x}$, $\delta {a}_{y}$ | Wireless transmission distance, maximum wireless transmission distance, change in x direction displacement, change in y direction displacement, change in x direction velocity, change in y direction velocity, change in x direction acceleration, and change in y direction acceleration, respectively |

${t}_{ij}$ | Wireless link lifetime between nodes i and j |

${\mathcal{D}}_{i},\text{}{t}_{trans,i},\text{}{t}_{q,i},\text{}{t}_{cont,i},\text{}{t}_{proc,i}$, ${t}_{control,i}$, ${t}_{prop,i}$ | Total one-hop delay of node i, transmission delay of last packet of node i, queuing delay of all other packets except last packet in node i, contention delay of last packet of node i, processing delay of last packet of node i, flow control delay of last packet of node i, and propagation delay of last packet of node i, respectively |

$\overline{{\mathcal{D}}_{iWL}},\text{}\overline{{\mathcal{D}}_{iWI}}$ | Average wireless one-hop link delay of node i using CSMA–CA, and average wired one-hop link delay of node i using CSMA–CD, respectively |

${t}_{trans,ij}$, ${t}_{q,ij}$, ${t}_{cont,ij}$, ${t}_{proc,ij}$, ${t}_{control,ij}$, ${t}_{prop,ij}$ | Transmission delay of jth packet of node i, queuing delay of jth packet in node i, contention delay of jth packet of node i, processing delay of jth packet of node i, flow control delay of jth packet of node i, and propagation delay of jth packet of node i, respectively |

${t}_{rts},\text{}{t}_{cts},\text{}{t}_{ack}$ | Transmission delay for transmitting RTS, CTS, and ACK packets, respectively |

${t}_{\text{}frame\phantom{\rule{0.277778em}{0ex}}space},\text{}B{O}_{ij},\text{}{t}_{collision,ij}$ | Frame spacing in CSMA, total random backoff time of the jth packet in ith node during contention period, and total collision duration during contention for jth packet in ith node, respectively |

S, $T[N,N,2]$, $X[N,N,2]$, $D[N,N,2]$, ${P}_{j}\left[k\right]$, ${V}_{j}\left[k\right]$ | Destination node, adjacency matrix of link lifetime, adjacency matrix of position, adjacency matrix of link delay, and the parent vector containing list of parent nodes and channel types (wired or wireless) in reaching destination node j from source node k, route valid time for the path from source node k to destination node j, respectively |

${\mathcal{C}}_{th},\text{}{T}_{th},\text{}D{R}_{WL},\text{}D{R}_{WI}$ | Network congestion threshold, link lifetime threshold, wireless data rate, and wired data rate, respectively |

${H}_{channel},\text{}{H}_{hom},\text{}{H}_{WL},\text{}{H}_{WI}$ | Normalized network per channel link entropy, homogeneous normalized entropy, normalized homogeneous link entropy with respect to wireless links, and normalized homogeneous link entropy with respect to wired links, respectively |

$\overline{{\mathcal{Q}}_{i}},\text{}{U}_{ij},\text{}\overline{\mathcal{Q}}$ | Average pending transmission packet distribution factor at node i, pending transmission factor of node i at jth time step, and average pending transmission packet distribution factor of the network, respectively |

$\overline{{\mathcal{R}}_{i}},\text{}{Y}_{ij}$ | Average queue size of node i, and queue size of node i at jth time step, respectively |

$\overline{{\rho}_{WL}},\text{}\overline{{\rho}_{WI}}$ | Average collision probability for wireless links, and average collision probability for wired links, respectively |

$\overline{{t}_{cont,WL}},\text{}\overline{{t}_{cont,WI}}$, $\overline{{t}_{cont,WL,max}}$, $\overline{{t}_{cont,WI,max}}$ | Average contention delay for CSMA–CA, average contention delay for CSMA–CD, maximum of average contention delay for CSMA–CA, and maximum of average contention delay for CSMA–CD, respectively |

$E,\text{}{T}_{slot},\text{}{T}_{c},\text{}C{W}_{min},\text{}C{W}_{max}$ | Maximum retransmission attempts during contention, slot time, collision duration, minimum contention window, and maximum contention window, respectively |

$\mathcal{C},\text{}{\mathcal{C}}_{WL},\text{}{\mathcal{C}}_{WI}$ | Normalized network contention, normalized wireless contention, and normalized wired contention, respectively |

${\mathcal{S}}_{i},\text{}{N}_{i},\text{}N,{N}_{iWL},\text{}{N}_{iWI},\text{}{N}_{v},\text{}{N}_{r}$ | Node i’s set of one-hop neighbors’ node addresses, total one-hop neighbors of node i, total number of nodes, number of wireless links connected to node i, number of wired links connected to node i, total number of vehicle nodes, and total number of RSU nodes, respectively |

f, ${f}^{\prime}$, ${f}^{\prime \prime}$ | Data collection frequency, optimization frequency, and routing frequency, respectively |

Parameter | Value |
---|---|

Network simulation | NS-3.35 |

Optimizer | GuRoBi 10.0.0 |

Deep Neural Network | TensorFlow 2.11.0 |

Plotting tool | MATLAB R2021a |

Mobility scenario generation | SUMO version $v1\_14\_1$ and OpenStreetMap |

Simulation time | 600 s per each run |

Maximum vehicles | 200 |

Maximum RSUs | 64 |

Maximum speed of vehicles | 0–60 km/h (Urban), 0–100 km/h (Non-urban), 0–250 km/h (autobahn) |

Transmission protocol | User Datagram Protocol (UDP) |

Communication channels | DSRC for (I2V, V2I, V2V), point to point between RSUs, CSMA from RSU to controller node, and LTE between vehicles and controller node |

Wifi-standard | IEEE 802.11p |

DSRC transmission power | 33 dBm (urban), 41 dBm (non-urban), 44 dBm (highway) |

DSRC OFDM data rate | 27 Mbps |

DSRC propagation loss model | Cost–Hata (urban), 3-log distance (non-urban, autobahn) |

DSRC propagation delay model | Constant speed propagation delay |

DSRC error rate model | Nist error rate model |

LTE pathloss model | Cost-Hata |

LTE maximum transmit power | 23 dBm |

LTE SRS periodicity | 2, 5, 10, 20, 40, 80, 160, 320 |

LTE fading model | Trace fading loss model |

LTE EPC data rate | 1000 Mbps |

RSU backbone data rate | 1000 Mbps |

RSU backbone delay | 10 $\mathsf{\mu}$s |

Payload size for routing | 104 bytes |

Broadcasting status data payload size | 104 bytes for proposed routing, 56 bytes for Dijkstra, 0 bytes for AODV |

Unicasting uplink status & metadata payload size | $24{\sum}_{m\in {\mathcal{S}}_{i}}({\mathcal{N}}_{m}{Z}_{m}+\frac{1}{3})+104({\mathcal{N}}_{i}+1)+8$ bytes for proposed routing, $24{\sum}_{m\in {\mathcal{S}}_{i}}\left({\mathcal{N}}_{m}{Z}_{m}\right)+56({\mathcal{N}}_{i}+1)$ bytes for Dijkstra, 0 bytes for AODV |

packet_in | 24 bytes for proposed routing, 0 bytes for Dijkstra and AODV |

FlowMod payload size | 76 bytes for proposed routing, 68 bytes for Dijkstra, 0 bytes for AODV |

Communication cost per byte | 1—wired, 2—DSRC, 40—Cellular |

Routing frequency | Variable (${f}^{\prime \prime}$) in the range [0.02, 5] |

Number of nodes | Variable ($\mathcal{N}$) in the range [4, 256] |

Link lifetime threshold | Variable (${T}_{th}$) in the range [0, 10] |

Network contention threshold | 0.5 |

Network Instance | ${\mathit{P}}_{\mathit{H}3}$ |
---|---|

Link lifetime threshold of {0.0, 4.0, 6.0, 8.0, 10.0} s | {0.19, 0.07, 0.30, 0.78, 1.00} |

Routing frequency of {0.02, 0.06, 0.10} Hz | {0.42, 0.28, 0.09} |

Network size of {4, 8, 16, 32, 64} nodes | {0.50, 0.32, 0.29, 0.20, 0.08} |

Urban mobility scenario at speed {0, 10, 40} km/h | {0.32, 0.19, 0.07} |

Rural mobility scenario at speed {0, 20, 40, 60, 80, 100} km/h | {0.16, 0.33, 0.29, 0.30, 0.25, 0.21} |

Autobahn mobility scenario at speed {0, 30, 50, 90, 130, 170, 210, 250} km/h | {0.30, 0.34, 0.39, 0.37, 0.35, 0.33, 0.32, 0.31} |

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**MDPI and ACS Style**

Wijesekara, P.A.D.S.N.; Gunawardena, S.
A Machine Learning-Aided Network Contention-Aware Link Lifetime- and Delay-Based Hybrid Routing Framework for Software-Defined Vehicular Networks. *Telecom* **2023**, *4*, 393-458.
https://doi.org/10.3390/telecom4030023

**AMA Style**

Wijesekara PADSN, Gunawardena S.
A Machine Learning-Aided Network Contention-Aware Link Lifetime- and Delay-Based Hybrid Routing Framework for Software-Defined Vehicular Networks. *Telecom*. 2023; 4(3):393-458.
https://doi.org/10.3390/telecom4030023

**Chicago/Turabian Style**

Wijesekara, Patikiri Arachchige Don Shehan Nilmantha, and Subodha Gunawardena.
2023. "A Machine Learning-Aided Network Contention-Aware Link Lifetime- and Delay-Based Hybrid Routing Framework for Software-Defined Vehicular Networks" *Telecom* 4, no. 3: 393-458.
https://doi.org/10.3390/telecom4030023