# A kind of novel RSAR protocol for mobile vehicular Ad hoc network

- 44 Downloads

## Abstract

MVANET (Mobile Vehicular Ad hoc Network) as one part of Mobile Vehicular Ad hoc Network (MANET) has the feature: unreliable communication link and frequent change of network topology. In order to improve the communication link reliability and efficient routing, a kind of novel RSAR (rewarding smart Ad hoc routing) protocol for Mobile Vehicular Ad hoc Network is presented in this paper. Based on our suggested model, the reliability of the communication link is assessed and design a novel routing protocol according to the strategy of deep learning. As a kind of machine learning approach, the D-Learning (Deep-Learning) algorithm can be helpful to get the reliable routing path. The advantage of the RSAR protocol is evaluated by the simulator and tests of the practical applications. The experimental results show that RSAR exhibits good results at a delivery rate, end-to-end delay and average hops compared with SLBF, QLAODV and GPSR.

## Keywords

Mobile vehicular Ad hoc networks Network topology Communication link Smart Deep learning## 1 Introduction

It is well known that MVANET (Mobile Vehicular Ad hoc Network) as one part of Mobile Vehicular Ad hoc Network (MANET) has the following feature: unreliable communication link and frequent change of network topology (Fukushima 2011; Weiss 2011; Liu 2019). MVANET is a very important topic on the problem of intelligent transportation system design. Nowadays, many research institutions have begun to focus on this research (Gao and Liu 2019; Seredynski et al. 2011; Al-Sultan et al. 2014; Lalitha and Rajesh 2014). In the intelligent transportation system, MVANET can achieve many security and non-security applications, such as security areas, safety information notification, road obstruction warnings, and accident avoidance applications, as well as applications such as in-car entertainment (Leung 2001; Liu et al. 2016; Abbasi et al. (2014); Wang and Song 2015), multimedia data sharing, remote controlling and telecommunications services in non-security areas. Routing protocols, as an important part of MVANET, which are a vital part of intelligent traffic. In order to meet the needs of different applications under different scenarios, it is a major problem to design a routing protocol that can adapt to different scenarios and has high reliability and low latency.

Because MVANET is a special MANET, many traditional MANET routing algorithms are used in the MVANET network. These routing algorithms can be divided into the following categories. Here are more representative of the four routing algorithms. They are respectively, active routing, reactive routing, geo-based beacon routing and geo-based beacon-less broadcast routing. Active routing algorithm, also known as table-driven routing algorithm, is more representative of the Destination node Sequence Distance Vector (DSDV) (Zhu 2012; Jerbi et al. 2009; Li et al. 2015; Eiza and Ni 2013) and Optimized Link State Routing (OLSR) (Yan and Olariu 2011; Zhang et al. 2014; Zhang 2012a, b; Chen et al. 2018) routing algorithm. This algorithm has a periodic routing packet broadcast, exchange routing information, and maintain a routing table containing routing information arriving at other nodes, regardless of whether there is a communication requirement. Reactive routing algorithm, also known as on-demand routing algorithm, is mainly worked by the route discovery and routing maintenance of the two processes. The representative algorithms are Ad hoc On-Demand Distance Vector (AODV) routing (Liu 2018; Tang 2019; Liu 2017) and Dynamic Source Routing (DSR) (Zhou 2018; Niu 2017; Zhang 2019; Zhang et al. 2014). Based on the geography-based routing algorithm, the researchers also proposed a beaconless broadcast routing algorithm based on geographical location. The representative algorithms are Self-adaptive and Link-aware Beaconless Forwarding protocol (SLBF), Timer Greedy Forwarding Algorithm (TGF) (Zheng and Zhang 2015; Zheng and Zhao 2016; Toutouh et al. 2012; Wu et al. 2010; Abboud and Zhuang 2014; Zhu et al. 2015; Chen et al. 2010; Beaulieu and Xie 2004; Cheng and Panichpapiboon (2012); Zhu and Li 2016) and so on.These routing protocols do not need to consider the topology changes, just using GPS to locate the destination node, and are designed by estimating traffic flow, but it does not consider the link reliability between nodes. Some reliable routing protocols are proposed, such as SLBF (Xue and Kumar 2004; Panichpapiboon et al. 2010) and EG-RAODV (Pascoe-Chalke et al. 2010; Darwish et al. 2018; Namboodiri and Gao 2007; Khasawneh et al. 2018). These protocols are designed by adding parameters as link reliability between nodes and packet error rate. It can improve the packet delivery ratio to a certain extent, but cannot guarantee the time delay.

In the aforementioned algorithms, the node also needs to use GPS to locate the destination node location. The sending node sends the data packet in the form of broadcast. The node does not need to maintain the neighbor table, but instead chooses the next hop forwarding node through the timer delay time, and becomes the node of the forwarding node and uses the broadcast form to suppress other node forwarding duplicate packets. Although this algorithm reduces the overhead by reducing the beacon packet, it takes the broadcast and timing so that it not only takes up many idle channels, wastes the transmission time, but also when the sending node increases and the amount of data is enlarged, it is easy to cause serious broadcast storm problems and difficultly guarantees the reliability of packet transmission.

In the literature (Wu et al. 2011; Ruiling et al. 2014), the authors proved by experiments that the distance between vehicles can be more consistent with real traffic flow and safety distance when it is lognormal distribution. Fully understanding of the traffic flow model and the motion characters of vehicles can help effectively evaluate the link reliability between nodes. In the literature (Sohail et al. 2018), the SL trust model, a subjective logic trust mechanism, was proposed, which reduced extra routing and computation overhead. The literature (Song et al. 2018) improved the current uneven clustering routing algorithm, which has a longer network life and better stability. The literature (Liu et al. 2018) presented a multi-source information fusion approach to detect bogus emergency messages, which can achieve a higher significantly detection rate. The literature (Qin et al. 2018) proposed a dynamic calculation method of the trust value of users, which has a better routing performance. With continuous researches of the routing protocol in recent years, some intelligent protocols (Zhang and Zhang 2018; Zhang et al. 2019; Zhang and Dong 2018; Zhu and Liu 2016; Hamed 2018; Ma 2017; Sujoy and Andrei 2014; Ahmed 2017; Liang and Ma et al. 2018) are applied in MVANET and have better results compared to traditional the routing protocols. As a self-learning algorithm, D-Learning (Deep -Learning) algorithm (Wu et al. 2010; Wu et al. 2011; Ruiling et al. 2014; Sohail et al. 2018; Song et al. 2018; Liu et al. 2018; Qin et al. 2018) can find the shortest path from the source node to the destination node through constant interaction with the outside environment. Based on this idea, a kind of novel rewarding smart Ad hoc routing protocol (RSAR) is proposed by using the D-Learning strategy in this paper. It can adaptively adjust D-Table with ensuring the reliability of each hop link to adopt such dynamic network topology as MVANET.

MVANET as a kind of mobile ad hoc network (MANET), its high-speed mobility of the nodes makes the network topology change frequently, and the transmission path can be easily interrupted, so the routing efficiency is lower. In order to improve the routing efficiency and traffic safety, and avoid the occurrence of traffic accidents (such as collision, rear-end), we present a reliable and adaptive routing protocol. The reliability of the whole link depends on the links between each hop. This paper establishes a reliable model of the link between nodes by a detailed study of the motion characteristics of vehicles. By calculating the probability of link reliability, the paper uses the result as a parameter in the D-Learning algorithm to design the RSAR protocol.

## 2 Modelling of MVANET

### 2.1 Interactive model of the vehicles

Considering a MVANET on a highway with no on or off ramps, all the vehicles in the road have the same transmission range, denoted by \( R \). Assuming that the transmission range is much longer than the width of the highway so that a node can communicate with any node within a longitudinal distance of less than \( R \) from it, therefore we ignore the width of the road (Abboud and Zhuang 2014; Zhu et al. 2015; Chen et al. 2010; Beaulieu and Xie 2004; Cheng et al. 2012; Zhu and Li 2016; Xue and Kumar 2004). The optimal route on the highway is also feasible. We still assume that there are acceleration, deceleration, changing lanes and overtaking on the road.

*D*in vehicles per kilometer (veh/km). Let

*μ*and

*σ*be the mean and the standard deviation of the distance headway in meters, respectively. Where

*μ*=1000

*/D*and

*σ*are constant system parameters and take different values according to the vehicle density. Let

*i*th distance headway, between node \( i - 1 \) and node \( i \), where

*X*

_{i}(

*m*) is a random variable representing the distance headway of node i at the

*m*th time step. At any time step,

*X*

_{i}(

*m*)∈[

*α*,

*X*

_{max}] for all

*i*≥1,

*m*≥ 0. Where

*α*and

*X*

_{max}is the minimum and maximum inter-vehicle distances, respectively. Furthermore, assume that

*X*

_{i}is independent with identical statistical behaviors for all

*i*≥ 1. Besides, the distance headway between vehicles is log-normal distributed (Chen et al. 2010) and subject to

*X*

_{i}∈

*logN*(

*μ*

_{i},

*δ*

_{i}). In Fig. 1, we regard node

*V*

_{s}as the reference node, then

*X*represents the distance between

*V*

_{s}and any other node, where

*X*is also log-normal distributed (Beaulieu and Xie 2004).

*W*, the number of lanes be

*m*, the transmission range be

*R*and the node density be

*λ*. Then, the probability of no nodes in the relay selection area is given by

### **Lemma 1**

*In a MVANET, let the inter*-

*vehicle spacing be exponentially distributed with the parameter*\( \lambda_{1} \)

*on*\( {\text{Lane}}_{1} \)

*and*\( \lambda_{2} \)

*on*\( {\text{Lane}}_{2} \),

*respectively*(Cheng et al. 2012).

*Let*\( R' = \delta R \),

*where 0*<

*δ*≤

*1*.

*Then, we have the following lemma:*

- 1.The cumulative distribution function (CDF)
*F*of the spacing between the reference node and its nearest intra-level node (i.e., \( {\text{X}}_{\text{near}} \)) is given by$$ {\text{F}}_{{{\text{X}}_{Near} }} \left( x \right) = 1 - e^{{ - \lambda_{1} x}} ,x \in \left( {0,\infty } \right) $$(5) - 2.The CDF
*F*of the spacing between the reference and its nearest inter-level node (i.e., \( {\text{Y}}_{\text{near}} \)) is given by$$ {\text{F}}_{{{\text{Y}}_{Near} }} \left( x \right) = 1 - e^{{ - \lambda_{2} y}} - \lambda_{2} yEi( - \lambda_{2} y),y \in \left( {0,\infty } \right) $$(6)

### *Proof*

*F*of \( {\text{Y}}_{\text{near}} \) is given by

The proof is over.

### 2.2 Modelling of communication links

Absolutely, a vehicle can frequently change its speed and acceleration in the highway. However, it will not change its speed and acceleration all the time. When the surrounding vehicle is stable, the vehicle will maintain a certain speed for a period of time until it meets a diversion or obstacle and will consider changing its speed. When the surrounding road condition is wider than the open space, the vehicle can be selected to maintain the acceleration for a period of time. Therefore, it is of practical significance to assume that the vehicle will maintain a certain speed or maintain acceleration for a period of time in the link duration model.

The stability of the vehicle nodes is determined by the link stability in the learning process of the RSAR protocol. The criteria include the hop count, link quality and bandwidth between the nodes, and find the node with the highest link stability to forward information. If the current vehicle node link is disconnected or unstable, the learning process of the RSAR protocol will recalculate the link stability between the nodes, and select the vehicle node with the best link stability for information forwarding.

Taking into account that the movement of the vehicle is always in accordance with a fixed road, there are mainly two conditions when the link disconnected between two vehicle nodes. Assuming that in time *t*_{0}=0, vehicle *j* is in the one-hop communication range of vehicle *i*, and the vehicle *j* is located in front of vehicle *i*. The initial distance between two vehicles is a random variable *X*. The maximum communication radius of the vehicle is constant *R*. At the initial moment, *X* meet 0 ≤ *X *< R According to the system model, there will be acceleration, deceleration and overtaking of vehicles in a highway. The maximum speed limit on the road is set to *v*_{m} and all vehicles should move under or equal to *v*_{m}. Assuming that the acceleration of any vehicle at the beginning of the vehicle is *a*(0) and its speed is *v*(0). When *t * ≥ 0, the acceleration is defined as *a*(*t*) and the speed is defined as *v*(*t*).

### **Lemma 2**

*In a MVANET, let the inter*-

*vehicle spacing be exponentially distributed with the parameter*\( \lambda_{1} \)

*on*\( {\text{Lane}}_{1} \)

*and*\( \lambda_{2} \)

*on*\( {\text{Lane}}_{ 2} \),

*respectively. Let*\( R' = \delta R \),

*where 0*<

*δ*≤

*1*.

*Then, we have the following lemma*(Zhu and Li 2016).

- 1.The CDF
*F*of the spacing between the reference node and its farthest intra-level neighbor (i.e.,*X*) is given by$$ {\text{F}}_{\text{X}} \left( x \right) = \frac{{e^{{ - \lambda_{1} R}} \left( {e^{{ - \lambda_{1} x}} - 1} \right)}}{{1 - e^{{ - \lambda_{1} R}} }} $$(8) - 2.The CDF
*F*of the spacing between the reference node and its farthest inter-level neighbor (i.e.,*Y*) is given by$$ {\text{F}}_{\text{Y}} \left( x \right) = \frac{{e^{{ - \lambda_{2} \left( {R' - y} \right)}} \left( {1 - e^{{ - \lambda_{2} y}} - \lambda_{2} yEi\left( { - \lambda_{2} y} \right)} \right)}}{{1 - e^{{ - \lambda_{2} R'}} - \lambda_{2} R'Ei\left( { - \lambda_{2} R'} \right)}} $$(9) - 3.
The CDF

*F*of the second one-hop progress on Lane 1 (i.e., Z)is described bywhere \( x \in \left( {0,R} \right),y \in \left( {0,R'} \right),{\rm and} \)\( z \in \left( {R - x,R} \right). \)$$ {\text{F}}_{\text{Z|X}} \left( {z|x} \right) = \frac{{e^{{\lambda_{1} \left( {z - R} \right)}} - e^{{ - \lambda_{1} x}} }}{{1 - e^{{ - \lambda_{1} x}} }} $$(10)

### *Proof*

*i.i.d*. random variables (Zhu and Li 2016). Let the number of the reference node’s intra-level neighbors be

*N*, where

*N*is a non-negative integer.

*Then, the value of X is equal*\( X = \sum\nolimits_{i = 1}^{N} {S_{1,i} } \)

*. We have*\( \sum\nolimits_{i = 1}^{N - 1} {S_{1,i} } < X \le R \)

*and*\( \sum\nolimits_{i = 1}^{N + 1} {S_{1,i} } > R \). Let \( N_{{i,\left[ {a,b} \right]}} \) be the number of nodes in the range [

*a, b*] on \( Lane_{i} \). Then, we can calculate the CDF

*F*of

*X*by

*M*inter-level neighbors, where

*M*is a non-negative integer. Then, the distance between the reference and its farthest inter-level neighbor is given by \( Y = \sum\nolimits_{i = 1}^{M - 1} {S_{2,i} } \). We have \( \sum\nolimits_{i = 1}^{M - 1} {S_{2,i} } < Y \le R \) and \( \sum\nolimits_{i = 1}^{M + 1} {S_{2,i} } > R \). Thus, we can get the CDF

*F*of

*Y*by

- 1.If \( a\left( 0 \right) = 0 \), there is$$ v\left( t \right) = v\left( 0 \right) $$(13)
- 2.If \( a\left( 0 \right) > 0 \), there are$$ v\left( t \right) = \left\{ {\begin{array}{ll} {v\left( 0 \right) + a\left( 0 \right)t} & \quad {t \le \frac{{v_{m} - v\left( 0 \right)}}{a\left( 0 \right)}} \\ {v_{m} } & \quad {\text{else}} \\ \end{array} } \right. $$(14)
- 3.If \( a\left( 0 \right) < 0 \), there are$$ v\left( t \right) = \left\{ {\begin{array}{ll} {v\left( 0 \right) + a\left( 0 \right)t} & \quad {t \le \frac{ - v\left( 0 \right)}{a\left( 0 \right)}} \\ 0 & \quad {\text{else}} \\ \end{array} } \right. $$(15)

*v*(

*x*) at the time interval [0,

*t*] is defined as:

*i*and

*j*at time

*t*. Assuming that the initial speed and acceleration of vehicles

*i*and

*j*are

*a*

_{i}(0),

*v*

_{i}(0),

*a*

_{j}(0) and

*v*

_{j}(0) respectively; the instantaneous speed and acceleration of vehicles

*i*and

*j*at time

*t*are

*a*

_{i}(

*t*),

*v*

_{i}(

*t*),

*a*

_{j}(

*t*) and

*v*

_{j}(

*t*) respectively. We can get the distances of vehicles

*i*and

*j*at time interval [0,

*t*] as follows:

*t*= 0, the initial distance between vehicles

*i*and

*j*is

*X*, that is the distance

*d*

_{i,j}(0) =

*X*and the

*d*

_{i,j}(t) define is:

From formula (19) we can see obviously that when *d*_{i,j} > *R*, the link is disconnected.

*t*. Considering that

*t*is obtained as:

*i*or

*j*is in front. When

*j*is located in front of vehicle

*i*; On the contrary vehicle

*i*is located in front of vehicle

*j*. In order to effectively express that which vehicle is in front, we define a symbolic function as follows:

At this time there are 2 cases to calculate the link duration.

*I*(

*i*,

*j*) = 1, vehicle

*j*is located in front of vehicle

*i*. From formula (25) we know that

*t*as follows:

*I*(

*i*,

*j*) = − 1, vehicle

*i*is located in front of vehicle

*j*. From formula (25) we can get:

*t*as follows:

## 3 Novel RSAR protocol

In MVANET, by ensuring the reliability of each hop to achieve the reliability of the whole routing path, our RSAR protocol chooses the next hop to forward the node, it is not blind to select only the node that is the farthest from the sending node, but it takes into account the distance calculation of the destination node, the link state between the nodes and the effective node degree of the next one-hop. The optimal route is established according to the link reliability in the random walk topology, and the link reliability between the nodes is calculated according to the link reliability model. The method evaluates the link reliability between the vehicle nodes through the link maintenance time. The higher the link reliability of the vehicle nodes, the higher the reward value it receives. Find the best route from the source node to the destination node in a dynamic network such as a random walk topology.

According to the related works, D-Learning strategy is unsupervised self-learning. Through continuous interacting with the external environment, it can adaptively adjust its value to find the optimal path to reach the destination. That makes it be able to respond well to the dynamic MVANET. This part will describe the routing and forwarding strategy of augmented D-Learning algorithm in MVANET.

### 3.1 Augmented D-Learning strategy

In MVANET, because most of the time to send packets from node passes through multiple hops to reach the destination node, and it is very important to evaluate each hop link in order to ensure the reliability of the whole link. In order to enable the quality of the inter-node link to meet the requirement of packet transmission, the accuracy of the package and the link maintenance time are used to evaluate the quality of the link. With these two measures, we can accurately evaluate the status of the link between the sending node and the forward node. When choosing the next hop forwarding nodes, we consider the packet transmission delay time of the node, such as local optimization problem. Joining the forwarding nodes effectively neighbor node density can effectively solve these problems.

Augmented D-Learning can be used to our RSAR protocol, because it is a heuristic learning method based on the learning Agent. Generally speaking, in augmented D-Learning algorithm, the learning process of the Agent is mainly composed of a three tuple {*S,A,R*}, where *S *= {*s*_{1}*,s*_{2}*,s*_{3}*….s*_{n}} represents the state space; *A *= {*a*_{1}*,a*_{2}*,a*_{3}*,….a*_{n}} represents the activity space, and moving from one state to another is regarded as an effective activity; *R* represents the immediate reward for an activity, and the closer to the destination, the higher the reward of the activity was obtained. The detailed learning process is supported by Lemmas 1 and 2.

*S*of an Agent is composed of other vehicle nodes in its one hop range. The beacon packet in activity space

*A*is transmitted from one vehicle to another, which is defined as an activity. The immediate rewards

*R*which the Agent carries out an activity and get. The important symbols for the design of the RSAR protocol are shown in Table 1.

Important mathematical symbols

Symbols | Introduction |
---|---|

| One hop neighbor node set of destination node |

| The D-Value to be updated |

| The Agent node |

| The neighbor node of |

| The destination node |

| The |

| The reward value |

| The discount factor |

| The reliability of inter-node links |

| The number of packets which the node send and receive |

| The size of the packet and represented in a byte |

| The time interval |

*R*of the entire network as follows:

The rewarding value of the activity for all the neighbor nodes of the destination node is one. In the learning process, the rewarding value that may be obtained from a state transition to another state is indicated by D-Value D(s,a), (s∈S,a∈A) and its range is [0,1].

Each learning Agent maintains a two-dimensional table that records the destination node address that it can reach and the D-Value of the one-hop neighbor node similar to a matrix. This two-dimensional table is named D-Table (Ruiling et al. 2014; Sohail et al. 2018; Song et al. 2018; Liu et al. 2018; Qin et al. 2018). The columns of the table represent all the destination nodes that it can be reached, which is expressed by *D*_{i}; the rows of the table represent one-hop neighbor nodes, which is expressed by *N*_{i}. The *D(D*_{1}*,N*_{1}) represents the D-Value between itself and its neighbor node *N*_{1} when it reaches the destination node *D*_{1}. D-Table is a two-dimensional table, whose size is determined by the number of the neighbor nodes and the number of the destination nodes. It is obvious that it has good scalability. The value in the D-Table is updated by periodically exchanging beacon packets among nodes. The task of learning is distributed to each node, which makes the algorithm quickly converge to the optimal path based on the Lemmas 1 and 2, and the changes of the network topology can be timely adjusted.

*G*=

*{V,E}*as shown in the Fig. 2, where

*V*=

*{A, B ,C,….H}*represents the set of vehicle nodes. For vehicle node

*A*, its state space

*S*

_{A}is the set of all nodes that do not contain

*A*; The edge set

*E*represents a collection of nodes that can communicate directly in one hop range. We suppose

*A*of the Fig. 2 is the source node and

*G*is the destination node. Now we want to seek an optimal path from the sending node

*A*to the destination node

*G*through the way of D-Learning.

The aforementioned learning tasks are assigned to each vehicle node (which is the Agent), and the learning process is mainly to update the parameters of the D-Table, that is, to update the pair of state activity of the D-Value D(s, a), (s∈S, a∈A). Based on our analysis, we know that the bigger the number of hops is, the smaller the rewarding value is. So the final rewarding value is based on the number of hops, link reliability and bandwidth. By adding the factors of link reliability, the optimal path from source node to destination node can be obtained in the MVANET.

In Fig. 2, node *E* and *F* are one-hop neighbor nodes of the destination node *G*. The rewarding value from node *E* and *F* to destination node *G* can be represented as *D*_{E}*(G,G)* and *D*_{F}*(G,G)* respectively. Considering the effects of link quality and bandwidth, we suppose that their final D-Value are 0.8 and 0.9 respectively. The neighbor nodes of *D* are *A, B, C, E, F* and *H*. When *D* receives the beacon packets sent from any neighbor, the data packets are parsed, and the maximum D-Value to the destination node *G* is got, such as node *F*,\( \mathop {\hbox{max} }\nolimits_{{y \in N_{F} }} D_{F} \left( {G,Y} \right) \). Calculate the corresponding D-Value (*D*_{D}*(G,F)*), and update the D-Table. The *D*_{F}*(G,G)* is the largest and its value is one, which can be recorded in the beacon packet. A similar process will be done with data packets from other neighbor nodes, then one certain column in the D-Table can be updated. Considering the bandwidth and link reliability, suppose that we get *D*_{D}*(G,F) *=* 0.6* and the D-Table of the other neighbor nodes are updated. With the constant receive beacon packets, the node *D* constantly updates its D-Table. Similarly, when the node *D* sends its beacon packet, it extracts a certain column in the D-Table, finds the maximum D-Value between the node *D* and its neighbor node to send out. When the node *A* receives the beacon packet sent from the node D, it extracts the maximum D-Value and carries on the computation to update the D-Value *D*_{A}*(G, D)* in its D-Table. The same process will be done when it receives data packets from other neighbor nodes. Through the constant exchange of data packets, we will finally get the learning result. So we may easily find an optimal path from A to G, i.e., the path with the biggest D-Value of the node is the optimal path. That is to say, *A → B → E → G* is the path with the maximum D-Value, so it is the optimal path we want to choose. The process of dynamic update-and-save of the D-Table makes the strategy respond to the dynamic change of topology of MVANET quickly and ensure the reliability, and ensure good robustness of MVANET.

Of course, the link reliability could not totally represent the quality of the link of MVANET. The link reliability is only an index to measure link quality. The link reliability of the RSAR protocol proposed in this paper is not only a measure of the link quality between nodes but a combination of hop count, link quality and bandwidth to measure the link reliability. It is necessary to use it in an actual dynamic network topology. By determining the link reliability, link disconnection can be avoided, and the accuracy and efficiency of selecting the appropriate node to forward information are improved. Therefore, the application scenarios are still extensive.

### 3.2 The novel RSAR protocol

#### 3.2.1 Main part of the protocol

- 1.
Firstly, when a source node of a MVANET sends a data packet, it looks for its own D-Table to see whether or not it has the next hop node to reach the destination which is based on Lemmas 1 and 2. If there is yes, then it chooses the neighbor node with the largest D-Value; if not, then it starts the route establishing process, which operates the following step of “Sub-process of route establishing”.

- 2.
Secondly, after the route of a MVANET is established, a basic path from source node to destination node is obtained, and the D-learning of some vehicle nodes is completed which is based on Lemmas 1 and 2. In order to seek the optimal path of the whole network topology of a MVANET and solve the network segmentation problem, the route maintenance process is started to maintain the end to end path dynamically as the following step of “Sub-process of route maintaining”.

- 3.
Thirdly, through the processes above the optimal path of the entire network topology of a MVANET is established. When a vehicle node receives or sends data packets, it implements the first step; otherwise, it will implement the second step.

- 4.
Finally, check the aforementioned results of a MVANET, if it is OK, then EXIT; otherwise, goto the step 1.

#### 3.2.2 Sub-process of route establishing

In a MVANET, while the source node want to send a data packet to the destination node, it checks the D-Table to see weather or not there is a next hop node to the destination node based on the formula (22), (28) and (30). If yes, it seeks a neighbor node with the maximum D-Value to the destination node, and forwards the data packet to it. If no, it starts the route discovery process which is based on Lemma 1. In the processing, the source node of MVANET sends a message *R_REQ* data packet by broadcasting to the entire network of a MVANET and starts a path request timer, where message *R_REQ* records all nodes’ *id* who has passed in the routing process. If the destination node receives the first *R_REQ* packet from the source node, it will store the packet, and the subsequently received packets are discarded. Using the message *R_REQ* data packet, the destination node can get the node *id* and then generate a *R_REP* data packet and write the reversed path into it. After waiting for a time slot, it will send the *R_REP* data packet to the nodes which are recorded in the reversed path *id* through the relative broadcast information. If a recorded node has received the data packet, it will modify the next hop node address, update the D-Table, and send out the data packet through the single hop broadcast mode, while the other non-destination nodes only modify the D-Table and drop the data packet if they receive it; until the message *R_REP* is sent to the destination node, i.e. When the source node receives the message *R_REP* packet, it will dismiss the request timer and update its D-Table. At this time, a path from the source node to the destination node can be found and updated the D-Table of the nodes on the path from the source node to the destination node.

#### 3.2.3 Sub-process of route maintaining

In a MVANET, if the first routing path has been established, the D-Table of its neighbor nodes adjacent to this path will also be updated. Based on the goal of ensuring the effectiveness of the path in the dynamic change of MVANET, it is important to start the route maintaining process. The main work of the route maintaining process is to dynamically update the D-Table which is based on Lemma 1 or Lemma 2 and solve the relative problem of network segmentation. Each node of MVANET periodically broadcasts the beacon packets to update the D-Table of the neighbor nodes, where the beacon data packet includes the parameters of the position, speed and *Max(D*-*Value)* of the node based on the formula (22), (28) and (30). The *Max(D*-*Value)* is defined in the learning process. If we want to keep the effectiveness of the update work, the transmission delay of the beacon packet should be set to a random value between [0.55,1]. The effective threshold time of the destination node should be given for the D-Table. If the time of a destination node has exceeded the given threshold time, it means the destination node may be invalid and can delete its corresponding column data of the D-Table. Based on the Lemma 2, we can known that if there is the emergence of network partition due to the movement of vehicles of MVANET, the RSAR can use the carry-and-forward strategy (Zhu and Li 2016; Xue and Kumar 2004) at the dividing node. So it may start the timer of the path request to broadcast the message *R_REQ* packet. If it has NOT received the message *R_REP* packet which is sent from the destination node before the timer is over, it means the destination node may be unreachable and so the source node is informed to ignore the transmitted data, otherwise, it should establish the routing path again.

During the maintaining process, each node of MVANET periodically broadcasts the beacon packets to update the parameters such as position, speed and Max (D-Value). Each time, it is selected from the D-Table that the maximum D-Value is worth the neighbor as the next hop forwarding node. The D-Value is determined by three factors: hop count, link reliability and bandwidth. The higher the D-Value of the vehicle node, the better the link quality is. Therefore, choosing the maximum D-Value can ensure the high reliability of the link. When the maximum reward value is chosen, the source node can reach the destination node directly through the one-hop neighbor node, so the cost is the least. Consequently, when the beacon broadcasting selects the largest D-Value, it can guarantee the high reliability and low cost of the link.

### 3.3 Mathematical analysis of the complexity of the algorithm

The complexity of the RSAR algorithm are analyzed in this section and explained into two aspects: time complexity and space complexity.

### **Corollary 1**

*The time complexity of the RSAR algorithm is*\( {\rm O}(n) \).

### *Proof*

The time complexity of the RSAR algorithm is determined by exchanging Hello beacon packets periodically and handling link failure. Routing discovery is not required for every data transfer. This route discovery process can be considered as the basic operation of the \( {\text{N}} \) nodes of the MVANET. In this algorithm, the worst case is that there are N-2 nodes between the source and destination, and communication between two nodes must rely on all intermediate nodes, thus the worst time complexity of this algorithm is \( {\rm O}(n) \). In the route discovery process, the source node sends a R_REQ data packet by broadcasting to the entire network and starts a path request timer. When the intermediate node has a path to the destination or the destination receives the R_REQ packet, the worst time complexity of this process is \( {\rm O}(n_{1} ) \). The generated R_REP packet is forwarded to the source to form a forward path. The worst time complexity of this process is \( {\rm O}(n_{2} ) \). When the link fails, the worst time complexity of processing link failure is \( {\rm O}(n_{3} ) \). Let \( n = n_{1} + n_{2} + n_{3} \), thus the time complexity of the RSAR algorithm is \( {\rm O}(n) \).

### **Corollary 2**

*The space complexity of the RSAR algorithm is*\( {\rm O}(n^{2} ) \).

### *Proof*

In the MVANET, the frequent movement of nodes will generate the link and link disconnection constantly. Therefore, it is more suitable to utilize the adjacency matrix of the graph to represent the nodes. Each node in this algorithm stores the information and routing of neighbor nodes, thus the space complexity is \( {\rm O}(n^{2} ) \).

The RASR and the D-Learning algorithm have the same complexity. The RSAR algorithm improves packet transmission rate and reduces transmission delay by adding a reliability factor into D-Learning.

Although the performance improvement of the D-Learning algorithm is not reflected by complexity analysis, the improved RSAR protocol based on D-Learning algorithm shows better effect such as transmission rate, end-to-end delay and average hop count than D-Learning algorithm. In dynamic network topology, the D-Learning algorithm finds the shortest path from the source node to the destination node through dynamic continuous interaction with the surroundings. The RSAR protocol improves the D-Learning algorithm, adds a reliability factor to the algorithm, and adaptively adjusts to the D-Table to ensure the reliability of each hop link. The RSAR protocol increases the calculation of the link reliability probability than the D-Learning algorithm, which solves the problem of severe topology changes and unreliable links between vehicles. The RSAR protocol adds link reliability calculations, but does not increase the complexity of the algorithm, and is also a performance improvement of the D-Learning algorithm.

## 4 Real scene experiments

In this section, we use the real scene of a MVANET to verify the performance of the RSAR protocol or algorithm. The real environment parameters are illustrated in the table, and then we analyze the experiment results and give the conclusions.

### 4.1 Real scene parameters

Parameter of the MVANET

Parameters | Values |
---|---|

Size of topology (m) | 2000 × 2000 |

MAC standard | IEEE 802.11 MAC (2Mbps) |

Transmission range (m) | 160 |

Propagation model | Two-ray ground |

Practical time (s) | 600 |

CBR packet size (byte) | 1024 |

Data rate (packet/s) | 20 |

### 4.2 The results of the tests

The Fig. 3 has shown the relationship between the end-to-end time delay and the speed in a MVANET, where the time delay only calculates the average value of time taken by the destination node of a MVANET to receive the valid data packets. We can see that with the increase of the speed of the vehicle node, the time delay of four protocols totally has a rising trend, among these trends, the time delay of our RSAR protocol is between that of GSPR and SLBF. If the maximum speed is greater than 70 km/h, the time delay of SLBF increases rapidly and is more than that of our RSAR, that is arouse by the influence of topology changes of MVANET, re-transmission data or re-calculation of the effective route path. But our RSAR protocol is less affected by the topology changes of MVANET, the path is based on the maximum D-Value, the shortest routing length and the most reliable link, so our RSAR protocol has the shortest time delay. The QLAODV protocol also uses machine learning model, but the increased speed makes it switch the path frequently in order to maintain the effective routing path, so the time delay of the packet delivery among the nodes is greatly increased.

The Fig. 4 has shown the relationship between the average route length and speed of a MVANET, where the route length is calculated by the average number of the hops which are taken from the valid data packets to the destination node. According to the results of the figure, we can see that the route length in our RSAR protocol is less than that of the QLAODV protocol, because it has NOT adopted the path transformation strategy in order to remain the whole path of a MVANET, and the decision of forwarding data is only used to select the node which has the maximum D-Value, so the time delay is much shorter than that of the QLAODV protocol. Our RSAR proposed has a stable route length because it has been embedded the storage-and-forwarding mechanism and the maximum D-Value selecting mechanism, so each selected path is the shortest path. Both the SLBF protocol and the GPSR protocol have been used the greedy strategy to forward the data packets, but if the speed of a MVANET is greater than 70 km/h, under the influence of the topology changes, the route length of the SLBF and the GPSR will increase.

The Fig. 5 has shown that the relationship between the average route length and the number of nodes of a MVANET. We can see from the results of the Fig. 5, with the increasing of the nodes of a MVANET, the average route length of the four protocols has shown a downward trend. The reason is which the number of effective forwarding nodes of a MVANET increases. The length of the route path of our RSAR protocol is much shorter than that of the QLAODV protocol. The reason of the very close length of RSAR, SLBF and GPSR is that the SLBF protocol and the GPSR protocol both use the greedy mechanism. With the increasing of the nodes of a MVANET, the next hop selected by our RSAR protocol is much closer to the farthest node of a MVANET.

From the Fig. 6, we can see that for different routing protocols, with the increase of the average speed, in the same topology environment of a MVANET, the routing protocol package delivery rate shows a downward trend. This is because with the increase of the average speed, the speed of the nodes becomes faster, so it results in a dramatic change in the network topology. It also makes the link extremely unstable and the delivery rate drop. For the different routing protocols, the delivery rate of the packets is reduced accordingly. GPSR reduced by about 19%, SLBF reduced by about 14%, RAR (which is our RSAR protocol in this paper) reduced by about 11%. In contrast, the RAR protocol has a smaller rate of decrease in the delivery rate of packets due to an increase in average speed.

From Fig. 7 we can see that the delay time of these three routing algorithms is relatively low, but we note that the algorithm in this paper is relatively stable delay time, and it is lower than the other two routing algorithms. For different protocols, the delay time of the packets increases correspondingly as the average speed increases. GPSR increased by about 66%, SLBF increased by about 23%, RAR (which is our RSAR protocol in this paper) increased by about 19%. In contrast, the RAR protocol has a smaller rate of delay for the packets caused by the increase in average speed.

The performance of RSAR protocol (node speed increasing)

Performance | QLAODV | SLBF | GPSR | RSAR |
---|---|---|---|---|

Delivery rate | 75.21% | 70.96% | 53.15% | 93.15% |

End-to-end delay | 60.03% | 51.28% | 24.16% | 38.94% |

Average hops | 77.48% | 65.04% | 53.86% | 59.84% |

The performance of RSAR protocol (node number increasing)

Performance | QLAODV | SLBF | GPSR | RSAR |
---|---|---|---|---|

Delivery rate | 87.30% | 78.54% | 72.99% | 94.89% |

End-to-end delay | 11.55% | 19.86% | 07.04% | 09.86% |

Average hops | 43.45% | 34.68% | 21.17% | 26.18% |

As the speed of vehicle nodes increases, the delivery rate of our RSAR protocol increases by 17.94, 22.19, and 40.00% compared with QLAODV, SLBF and GPSR respectively. The end-to-end delay of RSAR is reduced by 12.35% and 21.20% compared with SLBF and QLAODV respectively. The average hops of RSAR are reduced by 5.20% and 17.64% compared with SLBF and QLAODV respectively.

As the number of vehicle nodes increases, the delivery rate of the RSAR increases by 7.59, 16.35, and 21.90% compared with QLAODV, SLBF and GPSR respectively. The end-to-end delay of RSAR is reduced by 1.69% and 10.00% compared with QLAODV and SLBF respectively. The average hops of RSAR are reduced by 8.50% and 17.27% compared with SLBF and QLAODV respectively.

_{dg}) of a MVANET, transmission overhead (O

_{t}) of the data packet among nodes of a MVANET.

*MC*is the vehicle node size number of each evaluation group in a MVANET, such as MC = 15, 25, 35, 45, 55, 65, 75, and so on, which means there are relative vehicle nodes in a certain evaluation group of a MVANET. Integer parameter

*i*and

*j*are node index of each evaluation group in a MVANET. Parameter \( \alpha ,\beta \) are weight real value, \( \alpha ,\beta \)∈ [0, 1] and \( \alpha + \beta = 1 \), such as the default real value is \( \alpha = 0.55,\beta = 0.45 \).are the scalar real values of the overhead of the vehicle node

*i*and the vehicle node

*j*of each evaluation group in a MVANET.

Based on the basic computational complexity analysis strategies (Zhang and Zhang 2018; Zhang et al. 2019; Zhang and Dong 2018), from the aforementioned formula of this paper, we can know the computational complexity degree of the proposed method belongs to O*(n*). According to the Eq. (32), after being compared with the existing protocols, we can see that our protocol has a much higher performance than that of the Ref. (Zhu and Liu 2016), Ref. (Hamed 2018), Ref. (Ma 2017), which guarantees much higher QoS (such as much lower overhead, much less complexity degree, much smaller delay and transmission reliability) of a MVANET. At the same time, we have done other relative comparisons with the protocols or methods of the Ref. (Sujoy and Andrei 2014), Ref. (Ahmed 2017), Ref. (Liang et al. 2018). The relative comparison figures are ignored because the effects are similar as the above figures. So according to the above results, we can see that our proposed protocol has certainly improved on routing overhead, transmission delay, the rate of packet delivery, the rate of losing a packet, throughput and other performances for a MVANET.

In the RSAR protocol, the link maintenance time model is established by analyzing the vehicle motion of a MVANET in the model establishment phase, and the link reliability evaluation method is given. But mainly for the highway to model, the link disconnection caused by the intersection still needs further research. Secondly, in the design stage of the RSAR protocol, since the learning task is allocated to each vehicle node in the learning process, the routing overhead of a MVANET is relatively large, and the routing overhead needs to be further reduced.

## 5 Conclusions

For the goal of solving the problem of dramatic topology change and unreliable link caused by the fast movement of the vehicles in a MVANET, a kind of novel RSAR protocol for Mobile Vehicular Ad hoc Network is presented in this paper. Based on our suggested model, the reliability of the communication link is assessed and design a novel routing protocol according to the strategy of deep learning. As a kind of machine learning approach, the D-Learning algorithm can be helpful to get the reliable routing path. The advantage of the RSAR protocol is evaluated by the simulator and tests of the practical applications. The experimental results show that RSAR exhibits good results at a delivery rate, end-to-end delay and average hops compared with SLBF, QLAODV and GPSR. So the RSAR protocol can be used in many applications of MVANET.

## Notes

### Acknowledgements

This research work is supported by National Natural Science Foundation of China (Grant No. 61571328), Tianjin Key Natural Science Foundation (No.18JCZDJC96800), CSC Foundation (No. 201308120010), Major projects of science and technology in Tianjin (No.15ZXDSGX00050), Training plan of Tianjin University Innovation Team (No.TD12-5016, No.TD13-5025), Major projects of science and technology for their services in Tianjin (No.16 ZXFWGX00010, No.17YFZC GX00360), Training plan of Tianjin 131 Innovation Talent Team (No. TD2015-23).

### Compliance with ethical standards

### Conflict of interest

Author De-gan Zhang, Xiao-huan Liu, Yu-ya Cui, Lu Chen and Ting Zhang declare that they have no conflict of interest.

## References

- Abbasi, I.A., Nazir, B., Abbasi, A., Bilal, S.M., Madani, S.A., A traffic flow-oriented routing protocol for VANETs, Eurasip J. Wirel. Commun. Netw.
**2014**(121), 1–14 (2014). https://doi.org/10.1186/1687-1499-2014-121 Google Scholar - Abboud, K., Zhuang, W.H.: Stochastic Analysis of a single-hop communication link in vehicular Ad hoc networks. IEEE Trans. Intell. Transp. Syst.
**15**(5), 2297–2307 (2014)Google Scholar - Ahmed, L.S.: An adaptive cooperative caching strategy (ACCS) for mobile Ad hoc networks. Knowl. Based Syst.
**120**(15), 133–172 (2017)Google Scholar - Al-Sultan, S., Al-Doori, M.M., Al-Bayatti, A.H., Zedan, H.: A comprehensive survey on vehicular Ad hoc network. J. Netw. Comput. Appl.
**37**(1), 380–392 (2014)Google Scholar - Beaulieu, N.C., Xie, Q.: An optimal lognormal approximation to lognormal sum distributions. IEEE Trans. Veh. Technol.
**53**(2), 479–489 (2004)Google Scholar - Chen, X.Q., Li, L., Zhang, Y.: A markov model for headway/spacing distribution of road traffic. IEEE Trans. Intell. Transp. Syst.
**11**(4), 773–785 (2010)Google Scholar - Chen, C., Cui, Y.Y.: New Method of Energy Efficient Subcarrier Allocation Based on Evolutionary Game Theory, Mobile Netw. Appl.
**2018**(9), 1–10. (2018). https://doi.org/10.1007/s11036-018-1123-y MathSciNetGoogle Scholar - Cheng, L., Panichpapiboon, S.: Effects of intervehicle spacing distributions on connectivity of VANET: a case study from measured highway traffic. IEEE Commun. Mag
**50**(10), 90–97 (2012)Google Scholar - Darwish, T.S.J., Abu Bakar, K., Haseeb, K.: Reliable intersection-based traffic aware routing protocol for Urban areas vehicular Ad hoc networks. IEEE Intell. Transp. Syst. Mag.
**10**(1), 60–73 (2018)Google Scholar - Eiza, M.H., Ni, Q.: An evolving graph-based reliable routing scheme for VANETs. IEEE Trans. Veh. Technol.
**62**(4), 1493–1504 (2013)Google Scholar - Fukushima, M.: The latest trend of v2x driver assistance systems in Japan. Comput. Netw. Int. J. Comput. Telecommun. Netw.
**55**(14), 3134–3141 (2011)Google Scholar - Gao, J.X., Liu, X.H.: Novel approach of distributed & adaptive trust metrics for MANET. Wirel. Netw.
**3**, 1–17 (2019). https://doi.org/10.1007/s11276-019-01955-2 Google Scholar - Hamed, F.: Hybrid cost and time path planning for multiple autonomous guided vehicles. Appl. Intell.
**48**(2), 482–498 (2018)MathSciNetGoogle Scholar - Jerbi, M., Senouci, S.M., Rasheed, T., Ghamri-Doudane, Y.: Towards efficient geographic routing in Urban vehicular networks. IEEE Trans. Veh. Technol.
**58**(9), 5048–5059 (2009)Google Scholar - Khasawneh, A., Bin Abd Latiff, M.S., Kaiwartya, O., Chizari, H.: A reliable energy-efficient pressure-based routing protocol for underwater wireless sensor network. Wirel. Netw.
**24**(6), 2061–2075 (2018)Google Scholar - Lalitha, V., Rajesh, R.S.: AODV_RR: a maximum transmission range based Ad hoc on-demand distance vector routing in MANET. Wirel. Pers. Commun.
**78**(1), 491–506 (2014)Google Scholar - Leung, R., Liu, J.L., Poon, E., Baochun, L.: MP-DSR: a QoS-aware multi-path dynamic source routing protocol for wireless ad-hoc networks. In: Proceedings of LCN 2001. 26th Annual IEEE Conference on Local Computer Networks, pp. 132–41, 2001Google Scholar
- Li, C.L., Chen, Y., Han, X.L., Zhu, L.N., A self-adaptive and link-aware beaconless forwarding protocol for VANETs, Int. J. Distrib. Sens. Netw.
**2015**(8), 1–12 (2015). https://doi.org/10.1155/2015/757269 Google Scholar - Li, R., Li, F., Li, X., Wang, Y.: QGrid: Q-learning based routing protocol for vehicular ad hoc networks. In: Proceedings of IEEE International Performance Computing and Communications Conference (IPCCC), pp. 1–8, 2014Google Scholar
- Liang, J.W., Ma, M.D.: A filter model for intrusion detection system in vehicle Ad hoc networks: a hidden Markov methodology, Knowl. Based Syst.
**2018**(9):1–12 (2018). https://doi.org/10.1016/j.knosys.2018.09.022 Google Scholar - Liu, S.: Novel unequal clustering routing protocol considering energy balancing based on network partition and distance for mobile education. J. Netw. Comput. Appl.
**88**(15), 1–9 (2017). https://doi.org/10.1016/j.jnca.2017.03.025 Google Scholar - Liu, S.: Novel dynamic source routing protocol (DSR) based on genetic algorithm-bacterial foraging optimization (GA-BFO). Int. J. Commun Syst
**31**(18), 1–20 (2018). https://doi.org/10.1002/dac.3824 Google Scholar - Liu, S.: Dynamic analysis for the average shortest path length of mobile Ad hoc networks under random failure scenarios. IEEE Access
**7**, 21343–21358 (2019). https://doi.org/10.1109/ACCESS.2019.2896699 Google Scholar - Liu, J.Q., Wan, J.F., Wang, Q.R., Deng, P., Zhou, K.L., Qiao, Y.P.: A survey on position-based routing for vehicular Ad hoc networks. Telecommun. Syst.
**62**(1), 15–30 (2016)Google Scholar - Liu, J.Z., Pan, H., Zhang, J.B., Zhang, Q., Zheng, Q.S.: Detecting bogus messages in vehicular Ad-hoc networks: an information fusion approach. In: Proceedings of China Conference on Wireless Sensor Networks (CWSN), pp. 191–200, 2018Google Scholar
- Ma, Z.: Shadow detection of moving objects based on multisource information in internet of things. J. Exp. Theor. Artif. Intell.
**29**(3), 649–661 (2017)MathSciNetGoogle Scholar - Namboodiri, V., Gao, L.: Prediction-based routing for vehicular Ad hoc, networks. IEEE Trans. Veh. Technol.
**56**(4), 2332–2345 (2007)Google Scholar - Niu, H.L.: novel PEECR-based clustering routing approach. Soft. Comput.
**21**(24), 7313–7323 (2017)Google Scholar - Panichpapiboon, S., Ferrari, G., Tonguz, O.K.: Connectivity of Ad hoc wireless networks: an alternative to graph-theoretic approaches. Wirel. Netw.
**16**(3), 793–811 (2010)Google Scholar - Pascoe-Chalke, M., Gomez, J., Rangel, V., Lopez-Guerrero, M.: Route duration modeling for mobile ad-hoc networks. Wirel. Netw.
**16**(3), 743–757 (2010)Google Scholar - Qin, X.Y., Wang, X.M., Lin, X.G., Wang, L., Zhang, L.C.: An efficient routing algorithm based on interest similarity and trust relationship between users in opportunistic networks. In: Proceedings of China Conference on Wireless Sensor Networks (CWSN), pp. 273–284, 2018Google Scholar
- Seredynski, M., Bouvry, P.: A survey of vehicular-based cooperative traffic information systems, In: Conference Record—IEEE Conference on Intelligent Transportation Systems, pp 163–168, 2011Google Scholar
- Sohail, M., Wang, L.M., Bushra, Y: Trust model based uncertainty analysis between multi-path routes in MANET using subjective logic. In: Proceedings of China Conference on Wireless Sensor Networks(CWSN), pp. 319–332, 2018Google Scholar
- Song, G.G., Qu, G.L., Ma, Q., Zhang, X., Improved energy efficient adaptive clustering routing algorithm for WSN. In: Proceedings of China Conference on Wireless Sensor Networks (CWSN), pp. 74–85, 2018Google Scholar
- Sujoy, R., Andrei, S.: The multi-depot split-delivery vehicle routing problem: model and solution algorithm. Knowl. Based Syst.
**71**(11), 238–265 (2014)Google Scholar - Tang, Y.M.: Novel reliable routing method for engineering of internet of vehicles based on graph theory. Eng. Comput.
**36**(1), 226–247 (2019)Google Scholar - Toutouh, J., Garcia-Nieto, J., Alba, E.: Intelligent OLSR routing protocol optimization for VANETs. IEEE Trans. Veh. Technol.
**61**(4), 1884–1894 (2012)Google Scholar - Wang, X., Song, X.D.: New medical image fusion approach with coding based on SCD in wireless sensor network. J. Electr. Eng. Technol.
**10**(6), 2384–2392 (2015)Google Scholar - Weiss, C.: V2X communication in Europe—from research projects towards standardization and field testing of vehicle communication technology. Comput. Netw.
**55**(14), 3103–3119 (2011)Google Scholar - Wu, C., Kumekawa, K., Kato, T.: Distributed reinforcement learning approach for vehicular Ad hoc networks. Ieice Trans. Commun.
**E93B**(6), 1431–1442 (2010)Google Scholar - Wu, C., Ohzahata, S., Kato, T: Learning route from beaconing and interest dissemination in vehicular sensor networks. In: International Conference on Telecommunication Systems, Services, and Applications IEEE, pp. 49–54, 2011Google Scholar
- Xue, F., Kumar, P.R.: The number of neighbors needed for connectivity of wireless networks. Wirel. Netw.
**10**(2), 169–181 (2004)Google Scholar - Yan, G.J., Olariu, S.: A probabilistic analysis of link duration in vehicular Ad hoc networks. IEEE Trans. Intell. Transp. Syst.
**12**(4), 1227–1236 (2011)Google Scholar - Zhang, X.D.: Design and implementation of embedded un-interruptible power supply system (EUPSS) for web-based mobile application. Enterprise Inform. Syst.
**6**(4), 473–489 (2012a)Google Scholar - Zhang, D.G.: A new approach and system for attentive mobile learning based on seamless migration. Appl. Intell.
**36**(1), 75–89 (2012b)Google Scholar - Zhang, T.: Novel self-adaptive routing service algorithm for application of VANET. Appl. Intell.
**49**(5), 1866–1879 (2019)Google Scholar - Zhang, T., Dong, Y.: Novel optimized link state routing protocol based on quantum genetic strategy for mobile learning. J. Netw. Comput. Appl
**122**, 37–49 (2018). https://doi.org/10.1016/j.jnca.2018.07.018 Google Scholar - Zhang, D.G., Ge, H., Zhang, T.: New multi-hop clustering algorithm for vehicular Ad hoc networks. IEEE Trans. Intell. Transp. Syst.
**20**(4), 1517–1530 (2019). https://doi.org/10.1109/TITS.2018.2853165 Google Scholar - Zhang, D.G., Li, G., Zheng, K.: An energy-balanced routing method based on forward-aware factor for wireless sensor network. IEEE Trans. Industr. Inf.
**10**(1), 766–773 (2014b)Google Scholar - Zhang, D.G., Wang, X., Song, X.D.: A novel approach to mapped correlation of ID for RFID anti-collision. IEEE Trans. Serv. Comput.
**7**(4), 741–748 (2014a)Google Scholar - Zhang, T., Zhang, J.: A kind of effective data aggregating method based on compressive sensing for wireless sensor network. EURASIP J. Wirel. Commun. Netw
**159**, 1–15 (2018). https://doi.org/10.1186/s13638-018-1176-4 Google Scholar - Zheng, K., Zhang, T.: A novel multicast routing method with minimum transmission for WSN of cloud computing service. Soft. Comput.
**19**(7), 1817–1827 (2015)Google Scholar - Zheng, K., Zhao, D.X.: Novel quick start (QS) method for optimization of TCP. Wirel. Netw.
**22**(1), 211–222 (2016)Google Scholar - Zhou, S.: A low duty cycle efficient MAC protocol based on self-adaption and predictive strategy. Mob. Netw. Appl.
**23**(4), 828–839 (2018)Google Scholar - Zhu, Y.N.: A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the internet of things (IOT). Comput. Math. Appl.
**64**(5), 1044–1055 (2012)zbMATHGoogle Scholar - Zhu, L.N., Li, C.L.: Geographic routing in multilevel scenarios of vehicular Ad hoc networks. IEEE Trans. Veh. Technol.
**66**(12), 7740–7753 (2016)Google Scholar - Zhu, L.N., Li, C.L., Wang, Y., Luo, Z., Liu, Z., Li, B.B., Wang, X.B.: On stochastic analysis of greedy routing in vehicular networks. IEEE Trans. Intell. Transp. Syst.
**16**(6), 1–14 (2015)Google Scholar - Zhu, Y.N., Liu, S.: Multi-radio multi-channel (MRMC) resource optimization method for wireless mesh network. J. Inform. Sci. Eng.
**32**(2), 501–519 (2016)MathSciNetGoogle Scholar