Position-based routing protocol using Kalman filter as a prediction module for vehicular ad hoc networks

Computers and Electrical Engineering 83 (2020) 106599

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Computers and Electrical Engineering journal homepage: www.elsevier.com/locate/compeleceng

Position-based routing protocol using Kalman filter as a prediction module for vehicular ad hoc networksR Raj K Jaiswal Department of Computer Science & Information Systems, Birla Institute of Technology & Science Pilani, K K Birla Goa Campus, Goa, India

a r t i c l e

i n f o

Article history: Received 10 February 2019 Revised 16 February 2020 Accepted 17 February 2020

Keywords: Kalman filter Extended Kalman filter Location Position-based routing Prediction

a b s t r a c t A vehicle’s Global Positioning System (GPS) location is used as the Location_ID in positionbased routing protocols. The accuracy of GPS is a major concern in vehicular ad hoc networks (VANETs), as it is influenced by environmental and technical factors. Therefore, a vehicle’s actual location is different from its GPS location, with a margin of error of plus or minus 5–100 m. To reduce the effect of the margin of error on routing, this study evaluated the position-based routing protocol by using the Kalman filter (KF) and extended Kalman filter (EKF). Each of these was utilized individually as a prediction module for the purpose of improving the average delay (AD), packet delivery ratio (PDR), and throughput by minimizing location error. The KF and EKF prediction modules were implemented using C++ programming and the Eigen library, which were further incorporated into an NS-3.23 simulator. The proposed routing protocol performance was compared with crosslayer, weighted, and position-based routing (CLWPR) protocol in terms of PDR, AD, and throughput. © 2020 Elsevier Ltd. All rights reserved.

1. Introduction The aim of VANETs is to reduce roadside accidents, traffic, and fuel expenditure. They use a specifically designed IEEE 802.11p standard to enable vehicle-to-vehicle (V2V) and vehicle-to-roadside unit (RSU) communication. In addition, it aims to provide public amenity information such as nearby fuel stations and restaurants. In principle, VANETs and mobile ad hoc networks (MANETs) have common characteristics such as mobility and intermittent linking. Based on these principles, the VANET uses MANET routing protocols. However, the evaluation results revealed that routing protocols performed less efficiently when a VANET environment was exclusively applied. The design of the routing protocols of MANETs forces them to accommodate energy and computing power constraints. Thus, they are less efficient in situations in which high mobility and speeds are a factor. The findings of [1] regarding the topology-based routing protocol suggest that optimized link-state routing and ad hoc on-demand distance vector protocols are less suitable for use within VANET environments. These protocols maintain a network topology, and due to frequent node movement, the topology frequently fluctuates. In contrast, the network topology is not supported by the position-based routing protocol. It utilizes the vehicle location as the Location_ID, which is obtained using a satellite navigation device such as the Global Positioning System (GPS). However, R This paper is for regular issues of CAEE. cation by the Editor-in-Chief.Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. M. Shadaram. E-mail address: [email protected]

https://doi.org/10.1016/j.compeleceng.2020.106599 0045-7906/© 2020 Elsevier Ltd. All rights reserved.

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R.K. Jaiswal / Computers and Electrical Engineering 83 (2020) 106599

infrastructural or technical factors such as tunnels, high-rise buildings, or line-of-sight tend to produce errors that in turn affect the accuracy of the vehicle location. Consequently, a vehicle’s actual location differs from its GPS location by approximately 5–100 m, thereby impeding the routing performance. To address the challenge, this study evaluated the efficacy of position-based routing protocol by using the Kalman filter (KF) and extended Kalman filter (EKF) location prediction techniques. In position-based routing protocols, each vehicle broadcasts the GPS location as the Location_ID, which is maintained by the neighboring nodes in a Neighbor_Table. The vehicles become neighbors of other vehicles when they fall within their transmission range. The Location_ID is also registered by a location server deployed at prominent locations in the city. The location server issues the Location_ID of a node as needed when it is not available in the Neighbor_Table of the source node. In principle, if the source node determines that the destination Location_ID in the Neighbor_Table is within the transmission range, it employs a greedy forwarding technique to transmit the packet directly to the destination node. In the event the Location_ID is not within the transmission range, it identifies the nearest neighbor node. Occasionally packets are trapped in a local maximum issue, wherein a node seems to be the nearest when the destination node is out of transmission range. To resolve this issue, perimeter forwarding was applied in [2]; whereas, the carry forward method was applied in [3]. Routing performance is similarly affected by GPS location errors. The present study utilized KF and EKF location prediction techniques in an attempt to reduce the effect of location error on routing performance. In particular, the study aimed to improve the efficiency of position-based routing protocol by reducing delay via prediction techniques. The experiment in the study employed the KF and EKF prediction techniques individually. The significant contributions of this study are as follows: • It proposes the modification of traditional position-based routing protocol with KF- and EKF-based prediction techniques to minimize location errors. • The performance was measured using the Winner-II [4] and two-ray ground propagation models for transmissions measuring at a distance of 500 and 250 m under various traffic environments. • The PDR, throughput, and AD of the proposed routing protocol were compared with those of the CLWPR protocol. The remainder of the paper is organized as follows: Sections 2 and 3 discuss the literature review and system model, respectively. Section 4 highlights the description of the KF and EKF techniques, while Section 5 presents the proposed work. Sections 6 and 7 outline the simulation parameters and results, respectively. Section 8 presents the conclusion. Note that this study refers to “node” and “vehicle” interchangeably and “position” and “location” interchangeably. Additionally, KF/EKF denotes that either KF or EKF was individually applied in the prediction process. 2. Literature review This section comprises two subsections that review the literature related to position-based routing protocols for prediction in MANET and VANET environments, respectively, as follows: 2.1. Specific to MANET In [5], an equation of motion-based location prediction technique was applied along with a geographic routing protocol that employs the previous location and vehicle velocity to update the node location. Furthermore, [6] applied the equation of a motion-based location prediction technique in which each node appends the route request packet via a location update vector (LUV). Collective LUV information is subsequently utilized by a destination node to predict the topology via the equation of motion. A geographic routing protocol using machine learning-based prediction is proposed in [7]. Similarly, Chegin and Fathy [8] proposed a routing protocol that predicts the worst-case link duration by applying a prediction table based on the Manhattan mobility model. The work conducted in [9] evaluated the greedy perimeter stateless routing protocol, wherein the location error was taken into consideration. Likewise, [10] also evaluated the geographic routing protocol employing prediction to assess the effect of location error on the performance of the routing protocol. In [11], a routing protocol with a KF-based prediction technique was proposed to minimize location errors. 2.2. Specific to VANET A recent study [12] proposed a KF-based location prediction algorithm. In this study, the prediction results culminating from the proposed algorithm were compared with those obtained via the artificial neural network (ANN). The comparison revealed that KF-based prediction is more accurate than ANN. Likewise, an EKF-based location prediction algorithm was proposed in [13]. Based on the results of this study, it can be concluded that EKF yields more accurate prediction results than KF provides. Hence, based on the results of [12,13], EKF prediction is more precise in comparison to KF and ANN predictions, respectively. Nevertheless, both studies have utilized KF and EKF models for standalone prediction and have not used their prediction models in any VANET applications such as routing or cooperative driving. A geographical routing protocol with an equation of motion-based prediction technique was applied in [14]. Additionally, [15] utilized an alpha-beta-gamma filter and grey theory for prediction. The proposed work in [16] used an equation

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of motion-based prediction technique to design a hybrid location-based routing protocol. Similarly, [17] applied the KF in position-based routing protocol as a prediction module for highway scenario-based vehicle location prediction. The study in [18] predicted movement via an equation of motion-based prediction for link stability computation. A node nearer to the destination is selected as the next forwarding node. Additionally, [19] proposed a reactive prediction-based routing protocol. Theoretically, it employs location and velocity to predict the link expiration time; however, it supports only highway scenarios. Balico et al. [20] proposed a similar routing protocol that applies a predicted future location in order to forward the packet. The variable-order Markov model was employed for predicting vehicular mobility patterns in a routing protocol in [21]. In contrast, Li et al. [22] utilized the vehicle location history for location prediction on the basis of an equation of motion. Katsaros et al. [23] similarly proposed CLWPR which employs multi-hop and uni-cast based opportunistic forwarding. The CLWPR protocol selects the subsequent forwarding node based on minimum weight which is calculated using Euclidean distance, angle, utilization, and MACinfo . It does not incur any route maintenance cost due to the non-availability of a route discovery procedure. In this protocol, the HELLO packet which contains the velocity, heading angle, and location information of the vehicle is broadcasted by each node periodically. The location is predicted using the equation Post = Post−1 + Vt−1 ∗ dt, where Post−1 and Post are the previous and current locations, respectively, while dt and Vt−1 are the time differences and previous velocity, respectively. According to the literature review, research on KF- and EKF-based location prediction in a position-based routing protocol is limited. Therefore, this study employed KF- and EKF-based location prediction in a position-based routing protocol. 3. System model and assumptions First, assume x as the latitude and y as the longitude. Then, the four-tuple vehicle state X can be defined as [x, y, vx , vy ], where vx and vy depict the speed. Eq. (1) is used in the vehicle state computation, where Post−1 , Vt−1 , and at−1 are respectively the location, velocity, and acceleration at t − 1, while dt depicts the time difference in velocity change.

Post = Post−1 + Vt−1 ∗ dt +

1 at−1 dt 2 2

(1)

Hence, vehicle state X is defined as:

⎡ ⎤ x ⎢y⎥

X =⎣

vx ⎦ vy

⎡

⎤

xt−1 + vx ∗ dt ⎢yt−1 + vy ∗ dt ⎥

=⎣

vxt−1 vyt−1

⎦

(2)

Here, xt−1 , yt−1 , vxt−1 , and vyt−1 are the measured vehicle state components at t − 1. The assumption is made that the subject vehicles are fitted with essential devices such as onboard units and GPS, and that vehicle speed varies based on traffic conditions. In this experiment, the acceleration and steering angle did not change instantaneously with the change in the vehicle direction during a turn or lane change. 4. Overview of the Kalman filter and extended Kalman filter 4.1. Kalman filter A KF is essentially a mathematical tool that utilizes the feedback control approach to estimate the state of a system. To obtain measurement feedback, the system’s state is estimated at regular intervals. In other words, it predicts and corrects − the system state and groups the equations accordingly. The predicted state xˆt− is computed using the prior state xˆt−1 , as − shown in Eq. (3), and the estimated error covariance Pt is calculated using Pt−1 , as shown in Eq. (4), in the Prediction Step. In the Correction Step, the predicted state xˆt is obtained using the device measured state zt , predicted state xˆt− , and Kalman gain Kt , as depicted in Eq. (6). Most commonly, the Kalman gain Kt determines the prediction accuracy and is computed using the measurement matrix Ht , measurement noise R, and Pt− , as shown in Eq. (5). The error covariance Pt is calculated using the identity matrix I and Pt− in Eq. (7). In the Correction Step, Eqs. (6) and (7) yield the filter outcome as xˆt and Pt . To − obtain a high prediction accuracy, the outcome values in the Correction Step must be updated, with xˆt−1 and Pt−1 for the next iteration in the Prediction Step [13]. Prediction Step Location Prediction − xˆt− = Axˆt−1 + But−1 + wt−1

(3)

Error Covariance

Pt− = APt−1 AT + Q

(4)

4

R.K. Jaiswal / Computers and Electrical Engineering 83 (2020) 106599 Table 1 KF and EKF notations. Symbol

Description

A Pt− Pt Pt−1 − xˆt−1 xˆt− AT xˆt wt−1 B ut−1 Ht vt HtT Q R Kt zt I

Transition matrix Error covariance Estimated error covariance by using a KF Prior error covariance at t − 1 Prior location at t − 1 Estimated location At Transpose KF-estimated location Process noise Model matrix (Change in acceleration) Commanded input Measurement matrix Measurement noise Ht transpose Process noise covariance Measurement noise covariance Kalman gain Measured location Identity matrix

Correction Step Kalman Gain −1

Kt = Pt− Ht T (Ht Pt− Ht T + R )

(5)

Prediction on Measurement zt (Update)

xˆt = xˆt− + Kt (zt − Ht xˆt− )

(6)

Error Covariance (Update)

Pt = (I − Kt Ht )Pt−

(7)

Table 1 presents the different notations used in KF equations. To estimate the system state, a system process model must be designed with a minimal set of information. Hence, the process model in this work was defined using an equation of motion, as shown in Eqs. (1) and (3): − xˆt− = Axˆt−1

(8)

Since a system measurement model is generally obtained using a measurement device, in this work, it is defined as:

zt = Ht .xˆt− + vt

(9)

where vt represents the measured noise at t. 4.2. Extended Kalman filter Unlike KF, the EKF is specifically designed for a nonlinear system. However, the EKF uses partial differentiation in Eqs. (10) and (11) to linearize a nonlinear system. The Eqs. (10) and (11) defines the nonlinear system model for process and measurement [13]: − xˆt− = f (xˆt−1 , ut−1 , wt−1 )

(10)

zt = h(xˆt− , vt )

(11)

− xˆt−1

In Eq. (10), depicts the previously measured state, while ut−1 and wt−1 are the commanded input and noise in the model, respectively. In Eq. (11), xˆt− is the current measured state, while vt is the identified error. The computational steps involved in EKF-based location prediction are seen below: Prediction Step Location Prediction − xˆt− = f (xˆt−1 , ut−1 , wt−1 )

(12)

Error Covariance

Pt− = Ft Pt−1 FtT + Wt Q Wt T

(13)

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Correction Step Kalman Gain −1

Kt = Pt− Ht T (Ht Pt− Ht T + Vt RVt T )

(14)

Prediction on Measurement zt (Update)



xˆt = xˆt− + Kt zt − h(xˆt− , vt )



(15)

Error Covariance (Update)

Pt = ( I − Kt Ht ) Pt−

(16)

The EKF symbols used in Eqs. (12)–(16) are similar to the KF symbols presented in Table 1, while symbols, Ft , Wt , Ht , and Vt used in Eqs. (13) and (14) are the Jacobian matrix, defined as follows: − ∂ f (xˆt−1 , 0, 0 ) ∂x − ∂ f (xˆt−1 , 0, 0 ) Wt = ∂w

Ft =

(17) (18)

Ht =

∂ h(xˆt− , vt ) ∂x

(19)

Vt =

∂ h(xˆt− , vt ) ∂v

(20)

FtT , Ht T , Vt T , and Wt T are the transposed version of Ft , Wt , Ht , and Vt . The Jacobian matrix Ft is calculated through partial − − differentiation of f (xˆt−1 , ut−1 , wt−1 ) with respect to xˆt−1 , as shown in Eq. (21).

⎡

∂ f1 ⎢ ∂ x1 ⎢ ∂ f2 ⎢ ⎢ ∂ x1 ⎢ ∂f ⎢ . Ft = =⎢ ∂x ⎢ . ⎢ ⎢ . ⎢ ⎣∂ f 6 ∂ x1

∂ f1 ∂ x2 ∂ f2 ∂ x2

.

.

...

.

.

...

.

.

.

.

.

.

.

.

.

.

.

.

∂ f6 ∂ x2

.

.

...

⎤ ∂ f1 ∂ x4 ⎥ ∂ f2 ⎥ ⎥ ∂ x4 ⎥ ⎥ . ⎥ ⎥ . ⎥ ⎥ . ⎥ ⎥ ∂ f6 ⎦ ∂ x4

(21)

− where x = xˆt−1 , Similarly, the Jacobian matrix of Wt , Ht , and Vt are achieved through partial differentiation to − f (xˆt−1 , ut−1 , wt−1 ) with respect to wt−1 and h(xˆt− , vt ) with respect to xˆt− and vt , respectively. − ut−1 and wt−1 are considered to be zero. Hence, the process model in Eq. (12) becomes f (xˆt−1 , 0, 0 ), whereas the − h(xˆt , vt ) measurement model remains unchanged.

4.3. Kalman filter and extended Kalman filter implementation The KF and EKF prediction modules were implemented in C++ by using the Eigen library for linear algebra [24]. 4.3.1. KF initialization In Eq. (3), wt−1 and ut−1 are kept as zero, as stated earlier. Thus, the KF parameters are initialized by considering the following values: Initial values of At :

⎡

1 ⎢0 At = ⎣ 0 0

0 1 0 0

dt 0 1 0

⎤

0 dt ⎥ 0⎦ 1

(22)

where dt = 1 second, and Ht is initialized using the measured vehicle location zt . The GPS device generally provides the latitude and longitude information of the vehicle. Thus, Ht becomes:



1 Ht = 0

0 1

0 0



0 0

(23)

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R.K. Jaiswal / Computers and Electrical Engineering 83 (2020) 106599

X is initialized to zero, and to optimize the error margin in state estimation, it is preferable to maintain the error covariance Pt− large. Thus Pt− becomes:

⎡

10, 0 0 0 ⎢ 0 − Pt = ⎣ 0 0

0 10, 0 0 0 0 0

0 0 10, 0 0 0 0

⎤

0 0 ⎥ 0 ⎦ 10 0 0 0

(24)

The process and measurement noise values Q and R are determined as per the requirements of the system model. These values are usually determined using an ad hoc approach and are fixed when the optimal estimation result is obtained [25]. Hence, Q and R are initialized as follows:

⎡

0.1 ⎢0 Q =⎣ 0 0



R=

0 0.1 0 0

0.001 0

⎤

0 0 0.1 0

0 0 ⎥ 0 ⎦ 0.1



0 0.001

(25)

(26)

4.3.2. EKF initialization The initial values of Ft , Wt , Ht , Vt , Pt , Q, and R are as follows: − − f (xˆt−1 , 0, 0 ) is partially differentiated with respect to xˆt−1 to obtain the Jacobian matrix Ft . Here, dt is assumed to be one. Thus, Ft is initialized as follows:

⎡

1 ⎢0 Ft = ⎣ 0 0

0 1 0 0

dt 0 1 0

⎤

0 dt ⎥ 0⎦ 1

(27)

− Similarly, f (xˆt−1 , 0, 0 ) is differentiated partially with respect to wt−1 to obtain Wt . However, wt−1 is noted as zero, assuming negligible process error. Thus, in this experiment, Wt is taken as follows:

⎡

0 ⎢0 Wt = ⎣ 0 0

0 0 0 0

0 0 0 0

⎤

0 0⎥ 0⎦ 0

(28)

By implementing partial differentiation on h(xˆt− , vt ) with respect to xˆt− and vt , Jacobian Ht and Vt are obtained which are initialized as:

⎡

1 ⎢0 Ht = ⎣ 0 0 and

⎡

1 ⎢0 Vt = ⎣ 0 0

⎤

0 1 0 0

0 0 1 0

0 0⎥ 0⎦ 1

0 1 0 0

0 0 1 0

0 0⎥ 0⎦ 1

(29)

⎤ (30)

Pt− , Q, and R are initialized as shown in Eqs. (24) and (25). The experimental results revealed that both of the filters yielded reliable predictions. However, the EKF differentiated from KF because it rendered a more accurate prediction, nearly as accurate as the actual location. Hence, the EKF is more useful in applications in which location precision is a primary concern (e.g., automatic parking and collision avoidance systems) compared to other fields that do not require high-precision location prediction via an EKF (e.g., routing) [13]. The present study used both the filters as the prediction modules in a position-based routing protocol to verify the effect of location error margin on routing performance. Although the use of the KF and EKF entails a higher computation time than the use of the equation of motion, in VANET applications, energy is not a constraint since the computing devices utilize power from the vehicle’s battery. 5. Position-based routing protocol using KF/EKF as the prediction module In this protocol, the nodes use the Neighbor _T able to maintain neighbor node information. The Neighbor _T able contains the Location_IDs of the neighbor nodes. The nodes appear in the network by broadcasting the HELLO packet, which contains

R.K. Jaiswal / Computers and Electrical Engineering 83 (2020) 106599

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Table 2 Set of parameters for routing. CLWPR

PROPOSED

Prediction module = Normal MaxQueueTime = 10 s HelloInterval=1.5 s DynHello = False

Prediction module = KF and EKF MaxQueueTime = 10 s HelloInterval=1.5 s DynHello = False

Table 3 Set of parameters used in NS-3-23. Parameters

Values

Routing protocols Simulation time Radio propagation model Antenna model Modulation technique MAC type MAC rate Transmission range Data type CBR generation rate (Kbps) No. of vehicles No. of connections Transport protocol Packet size

CLWPR and PROPOSED 300 s Winner-II and two-ray ground Omni-directional antenna OFDM IEEE 802.11p 6 Mbps 250 and 500 m CBR 128 25, 50, 75, 100 30% of the vehicle population UDP 500 bytes

the Location_ID, velocity, and Node_ID information. Due to the nature of node dynamics, the node location changes frequently. As a result, the Location_ID quickly becomes obsolete. To address this issue, each node broadcasts the HELLO packet periodically to the neighbor node at an interval of 1.5 seconds. The Neighbor _T able keeps the Location_ID until it expires or it changes location. In the current experiment, the interval was fixed at 4.5 seconds. Before sending the packet, the node explores the Neighbor _T able to identify the destination Location_ID. The available destination Location_ID passes to the KF/EKF prediction module, yielding the predicted Location_ID, it subsequently updates the predicted destination Location_ID into the packet header. The availability of the Location_ID in the Neighbor _T able is not guaranteed for all the nodes because a node does not maintain the Location_ID if those nodes are outside of its transmission range. Under such circumstances, the source node sends a request to the location server to obtain the destination Location_ID. After receiving the destination Location_ID from the location server, the source node repeats its search of the Neighbor _T able for the nearest neighbor node to the destination based on Euclidean distance. It then passes the closest neighbor node Location_ID to the KF/EKF module in order to obtain the predicted Location_ID as shown in Algorithm 1. Algorithm 1 KF/EKF-based Location Prediction. 1: procedure KF/EKF Prediction 2: Search (Dest. Location_ID at t) 3: if (Dest. Location_ID is found) then 4: Compute (Location_ID using KF/EKF at t+1) 5: Update (Location_ID ← Predicted Location_ID) 6: Send (Packet with predicted Location_ID to the dest.) 7: else 8: Send (Request to location server to get dest. Location_ID) 9: Search (Neighbor node Location_ID, nearer to the dest.) 10: Compute (Neighbor node Location_ID using KF/EKF at t+1) 11: Update (Location_ID ← Predicted Location_ID) 12: Send (Packet to neighbor node with Predicted Location_ID) 13: end if 14: end procedure

6. Simulation parameters The current experiment used the NS-3.23 simulator patch of the CLWPR protocol for simulation. In the CLWPR protocol, the KF/EKF modules were used individually instead of utilizing the equation of motion for prediction. The parameters used in the CLWPR protocol are listed in Table 2; whereas those used in the NS-3.23 simulator are listed in Table 3. In the simulation, it is assumed that the location servers are deployed at prominent locations and junction points to retrieve the vehicle Location_ID when it is not found in the Neighbor _T able.

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R.K. Jaiswal / Computers and Electrical Engineering 83 (2020) 106599

Fig. 1. City road layout.

Fig. 2. Highway Layout.

To evaluate the effect of path loss on location prediction using the routing protocol, two propagation models were used, namely two-ray ground and Winner-II. The B1 scenario was considered in the Winner-II propagation model during this experiment, which is specifically designed to resemble a more realistic path loss model in the case of the city scenario compared with the two-ray ground model [4]. In this experiment, VanetMobiSim was used to obtain the vehicular mobility for 25, 50, 75, and 100 nodes deployed in an area of 10 0 0 ∗ 10 0 0 m2 for the city scenario and 30 ∗ 20 0 0 m2 for the highway scenarios, as shown in Figs. 1 and 2. The number of vehicles was constant during the simulation period, and the speed, acceleration, and deceleration of the vehicles varied according to the traffic conditions. Their values are presented in Table 4. Furthermore, homogeneous and heterogeneous traffic environments were used in mobility generation. The homogeneous traffic environment comprises of only cars with equal body length. The parameters used to generate homogeneous traffic are listed in Tables 4a and 4b. The heterogeneous traffic environment is composed of trucks, cars, and buses. It is known that these vehicles have different speeds, accelerations, and decelerations in the city scenario, and the vehicle length is dependent on the vehicle type; thereby affecting the location prediction. Table 4 depicts a set of different parameters used to get heterogeneous traffic traces in VanetMobiSim. Each node in the simulation has an independent location error, which is introduced by using Gaussian distribution with zero mean. 7. Results and discussion The performance of the proposed routing protocol was measured and compared with that of the CLWPR protocol in terms of PDR, AD, and throughput metrics. The performance results were grouped into heterogeneous and homogeneous traffic environments for the city and highway scenarios, which were further subdivided into Winner-II and two-ray ground models. The simulation results revealed that both KF and EKF yielded similar PDR, AD, and throughput. However, EKF requires more computation time to complete the simulation than KF due to the involvement of additional computational steps such as Jacobean computation.

R.K. Jaiswal / Computers and Electrical Engineering 83 (2020) 106599

Table 4 Set of parameters used for mobility. (a) Common Parameters Description

Values

Simulator Simulation time Simulation area (city) Simulation area (highway) No. of Lanes Traffic light duration Traffic lights (city) Min. congestion distance Traffic type

VanetMobiSim 499 s 1000 ∗ 1000 m2 30 ∗ 2000 m2 2 60 s 10 2m Heterogeneous Homogeneous

(b) Set of Parameters for Cars Description Contribution in traffic Speed (min.) Speed (max.) Acceleration (max.) Deceleration (max.) Driver politeness factor Safe headway time Car length

Values 60% 0.1 m/s 25 m/s 0.9 m/s2 0.6 m/s2 0.8 2s 4m

(c) Set of Parameters for Buses Description Contribution in traffic Speed (min.) Speed (max.) Acceleration (max.) Deceleration (max.) Safe headway time Driver politeness factor Bus Length

Values 30% 0.1 m/s 17 m/s 0.5 m/s2 0.3 m/s2 5s 0.4 8m

(d) Set of Parameters for Truck Description Contribution in traffic Speed (min.) Speed (max.) Acceleration (max.) Deceleration (max.) Safe headway time Driver politeness factor Truck length

Values 10% 0.1 m/s 11 m/s 0.3 m/s2 0.5 m/s2 10 s 0.2 12 m

Fig. 3. PDR with Heterogeneous Traffic.

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R.K. Jaiswal / Computers and Electrical Engineering 83 (2020) 106599

Fig. 4. PDR with Homogeneous Traffic.

Fig. 5. Throughput with Heterogeneous Traffic.

Fig. 6. Throughput with Homogeneous Traffic.

Fig. 7. Average Delay (AD) with Heterogeneous Traffic.

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Fig. 8. Average Delay (AD) with Homogeneous Traffic.

For this reason, Figs. 3–8 do not depict the EKF results to avoid redundancy1 . A common legend is provided for Figs. 3– 8 in which 250 and 500 are attached to the terms PROPOSED and CLWPR to depict the transmission ranges. In addition, C and H are used as abbreviations for the city and highway scenarios. 7.1. PDR “PDR” represents the ratio of the number of packets received and sent. The subsequent subsections discuss the PDR in different scenarios. 7.1.1. City-based heterogeneous traffic Fig. 3a and b show the PDR on the two-ray ground and Winner-II propagation models for the city-based heterogeneous traffic. In these figures, PROPOSED-250 had a PDR of 52% with 25 nodes, and CLWPR-250 had a PDR of 54%. The value decreased to 14% for PROPOSED-250 and 13% for CLWPR-250 with 100 nodes by utilizing the two-ray ground model instead. However, PROPOSED-250 yielded improved PDR with 50 and 75 nodes compared to the CLWPR protocol, as shown in Fig. 3a. Nevertheless, the overall performance of CLWPR-500 was higher than that of PROPOSED-500. When using the Winner-II model, PROPOSED-500 yielded a higher overall PDR than CLWPR-500, while PROPOSED-250 produced lower PDR than CLWPR-250, as shown in Fig. 3b. 7.1.2. City-based homogeneous traffic With reference to the homogeneous traffic environment, it was determined that PROPOSED-250 and PROPOSED-500 yielded high PDRs in nearly all types of networks when compared with CLWPR-250 and CLWPR-500. PROPOSED-250 and CLWPR-250 yielded a PDR of 56% and 54% with 25 nodes by applying the two-ray ground model. However, the PDR decreased proportionally as the number of nodes increased. PROPOSED-50 0 and CLWPR-50 0 yielded a PDR of 94.9% and 95.7% with 25 nodes. However, PROPOSED-500 yielded an improved PDR in comparison to CLWPR-500 with an increase in the number of nodes, as shown in Fig. 4a. Using the Winner-II model, it was observed that the PDR increased in PROPOSED-250 as the number of nodes increased, while it did not in CLWPR-250. In comparison, PROPOSED-500 yielded a low PDR with 25 and 50 nodes, as shown in Fig. 4b. A comparison of Figs. 3a and 4b and Figs. 3b and 4b showed that location prediction improved the PDR in both the traffic environments for the city scenario. 7.1.3. Highway-based heterogeneous traffic From Fig. 3c, it is clear that PROPOSED-500 yielded higher PDR than CLWPR-500, unlike in the transmission range of 250 m, wherein CLWPR-250 had higher PDR on the two-ray ground model. When utilizing the Winner-II model, PROPOSED-250 and PROPOSED-500 yielded higher PDR than CLWPR-250 and CLWPR-500, as shown in Fig. 3d. 7.1.4. Highway-based homogeneous traffic With homogeneous traffic in the highway scenario, PROPOSED-250 and PROPOSED-500 yielded improved PDRs in all network sizes, with exception to PROPOSED-250 on the two-ray ground model, as shown in Figs. 3c, 4d. A comparison of Figs. 3c, 4c and Figs. 3d, 4d revealed that location prediction improved the PDR in a heterogeneous traffic environment for the highway scenario. Although prediction improves the PDR in the majority of scenarios, it was observed that PDR decreased when the number of nodes increased in all scenarios in both CLWPR and PROPOSED protocols. 1 In the results, PROPOSED-250, PROPOSED-500, CLWPR-250, and CLWPR-500 are used to explain the evaluation results. This means that the performance is evaluated at transmission ranges of 250 and 500 m.

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7.2. Throughput “Throughput” measures the average amount of data received by each node. Figs. 5, 6 show the throughput performance in both the city and highway scenarios. 7.2.1. City-Based heterogeneous traffic Fig. 5a, b show the throughput performance in heterogeneous city traffic. As shown in Fig. 5a, PROPOSED-250 yielded a higher throughput than CLWPR-250; however, both the protocols yielded equal throughputs on the two-ray ground model in 500 m transmission range. For 25 nodes, PROPOSED-250 yielded a throughput of 66.30 kbps; whereas CLWPR-250 yielded a throughput of 67.9 kbps. In this scenario, as the number of nodes increased, PROPOSED-250 improved the throughput in comparison to CLWPR-250. The throughput eventually reached 13.5 and 11 kbps for PROPOSED-250 and CLWPR-250 with 100 nodes, respectively. The throughput was comparable for the 500 m transmission range in both the protocols. When utilizing the Winner-II model, PROPOSED-500 yielded a higher throughput than CLWPR-500, while CLWPR-250 yielded a higher throughput than PROPOSED-250, as shown in Fig. 5b. 7.2.2. City-based homogeneous traffic It is apparent from Fig. 6a that PROPOSED-250 yielded a higher throughput with 25 and 50 nodes on the two-ray ground model. However, it decreased with 75 and 100 nodes. PROPOSED-250 and CLWPR-250 generated maximum throughputs of 71.61 and 70.01 kbps, respectively, with 25 nodes; whereas, minimum throughputs of 26.51 and 28.62 kbps were detected with 100 nodes. However, PROPOSED-500 yielded a higher throughput than CLWPR-500. When utilizing the Winner-II model, both PROPOSED-250 and PROPOSED-500 yielded elevated throughputs in all scenarios. PROPOSED-50 0 and CLWPR-50 0 yielded maximum throughputs of 105.40 and 97.10 kbps with 25 nodes, respectively, and minimum throughputs of 50.85 and 47.09 kbps with 100 nodes as shown in Fig. 6. The throughput decreased respectively as the number of nodes increased (Fig. 6a and b). Figs. 5a, b, 6a and b substantiate that the throughput is high for homogeneous traffic in comparison to heterogeneous traffic. 7.2.3. Highway-based heterogeneous traffic In both propagation models, the throughput was improved in heterogeneous traffic scenarios. PROPOSED-250 and CLWPR250 yielded maximum throughputs of 48.46 and 50.45 kbps, respectively, with 25 nodes on the two-ray ground model. However, the proposed model improved the performance as the number of nodes increased. PROPOSED-250 and CLWPR-250 yielded minimum throughputs of 44.46 and 42.86 kbps, respectively, with 100 nodes. Overall, the throughput was improved with PROPOSED-500 in comparison to CLWPR-500, as shown in Fig. 5c. When utilizing the Winner-II model, both PROPOSED-500 and CLWPR-500 yielded maximum throughputs of 111.84 and 114.36 kbps with 25 nodes and minimum throughputs of 54.81 and 52.39 kbps with 100 nodes, respectively, as shown in Fig. 5d. 7.2.4. Highway-based homogeneous traffic When utilizing the two-ray ground model, maximum throughputs of 50.45, 48.46, 124.87, and 124.07 kbps were observed with 25 nodes for CLWPR-250, PROPOSED-250, CLWPR-50 0, and PROPOSED-50 0, respectively. With 100 nodes, the throughputs reach a minimum of 44.86, 42.61, and 68.43, and 69.88 kbps for CLWPR-250, PROPOSED-250, CLWPR-500, and PROPOSED-500, respectively, as shown in Fig. 6c. With reference to the Winner-II model, maximum throughputs of 57.22, 54.08, 111.84, and 114.36 kbps were observed with 25 nodes for CLWPR-250, PROPOSED-250, CLWPR-500, and PROPOSED-500. Similarly, minimum throughputs of 44.06, 45.38, 54.81, and 52.39 kbps were obtained with 100 nodes, as shown in Fig. 6d. Despite these variances, improved throughputs were observed in both scenarios. 7.3. AD “AD” measures the end-to-end delay between two communicating nodes. Because the location prediction technique estimates the probable location of the nodes, it minimizes the AD by providing an accurate location. Figs. 7 and 8 illustrate the AD with different scenarios as described below. 7.3.1. City-based heterogeneous traffic It is evident from Fig. 7a and b that the location prediction in routing minimizes the AD. The measured ADs were 0.1743, 0.0536, 0.0023, and 0.0024 ms for CLWPR-250, PROPOSED-250, CLWPR-500, and PROPOSED-500, respectively, with 25 nodes on the two-ray ground model. These values increased proportionately as the number of nodes increased. The maximum ADs were recorded with 100 nodes: 1.3753, 0.8620, 1.1589, and 1.13754 ms for CLWPR-250, PROPOSED-250, CLWPR-500, and PROPOSED-500, respectively, as shown in Fig. 7a. When utilizing the Winner-II model, the minimum ADs were recorded at 0.2417, 0.02714, 0.0357, and 0.0384 ms for CLWPR-250, PROPOSED-250, CLWPR-50 0, and PROPOSED-50 0, respectively, with 25 nodes. These values increased with 100 nodes to 0.4488, 0.4876, 1.2132, and 1.0894 ms, as shown in Fig. 7b.

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7.3.2. City-based homogeneous traffic When applied to homogeneous traffic, the proposed routing protocol yielded lower AD than the CLWPR, as shown in Fig. 8a and b. With reference to the two-ray ground model, the minimum ADs were observed with 25 nodes: 0.1994, 0.1654, 0.0132, and 0.0628 ms for CLWPR-250, PROPOSED-250, CLWPR-500, and PROPOSED-500, respectively. In the same order, the values increased with 100 nodes to 1.5315, 1.3509, 1.1368, and 1.0659 ms, as shown in Fig. 8a. When utilizing the Winner-II model, the minimum ADs were 0.1449, 0.1165, 0.2189, and 0.1567 ms; the maximum ADs with 100 nodes were 1.036, 0.6910, 1.376, and 1.367 ms for CLWPR-250, PROPOSED-250, CLWPR-500, and PROPOSED-500, respectively, as shown in Fig. 8b. It was observed that the two-ray ground model yielded lower AD than the Winner-II model as a whole.1 7.3.3. Highway-based heterogeneous traffic The minimum ADs in this scenario for CLWPR-250, PROPOSED-250, CLWPR-50 0, and PROPOSED-50 0 were 0.1743, 0.053, 0.0 024, and 0.0 023 ms. The maximum ADs on the two-ray ground model were 0.9453, 0.8620, 0.8589, and 0.7754 ms, respectively, as shown in Fig. 7c. The Winner-II model revealed similar results. The minimum ADs were 0.1614, 0.1743, 0.0 06, and 0.0 07 ms with 25 nodes, and the maximum ADs were 0.6634, 0.4977, and 0.9473, and 0.8572 ms with 100 nodes for CLWPR-250, PROPOSED-250, CLWPR-500, and PROPOSED-500, respectively, as shown in Fig. 7d. 7.3.4. Highway-based homogeneous traffic With 50 and 75 nodes in this scenario, PROPOSED-250 yielded a higher AD than CLWPR-250 on the two-ray ground and Winner-II models, respectively. With exception to these delays, prediction reduced the delay in all other scenarios. The minimum ADs observed for CLWPR-250, PROPOSED-250, CLWPR-50 0, and PROPOSED-50 0 were 0.1743, 0.0536, 0.0024, and 0.0023 ms. The maximum ADs were 0.9453, 0.762, 0.9789, and 0.75 ms on the two-ray ground model, as shown in Fig. 8c. When utilizing the Winner-II model similar results were observed. The minimum ADs were 0.1714, 0.1643, 0.2189, and 0.0072 ms, while the maximum ADs were 1.558, 1.364, 0.9473, and 0.8572 ms, as shown in Fig. 8d. 8. Conclusion The experiment results revealed that both the Kalman filter- and extended Kalman filter-based prediction produced similar packet delivery ratio, throughput and average delay results. However, the extended Kalman filter predictions were more accurate than the Kalman filter predictions. Even though the extended Kalman filter provides the precise location, it consumes more computation time to complete the simulation in comparison with the Kalman filter due to the additional computational steps involved, such as Jacobean computation. With respect to routing performance, it was observed that PROPOSED-500 yielded satisfactory and consistent packet delivery ratio in comparison with CLWPR-500, unlike PROPOSED-250. PROPOSED-500 yielded a higher throughput in all scenarios; whereas, PROPOSED-250 reduced the throughput on the two-ray ground model. Nevertheless, location prediction enhanced the overall throughput. It was also discovered that location prediction minimized the average delay in PROPOSED250 and 500 in comparison with CLWPR-250 and 500. In both heterogeneous and homogeneous traffic environments, the packet delivery ratio, throughput, and average delay were nearly identical. This finding proves that location prediction is not affected by the traffic environment. When the highway and the city traffic environments were compared, the city results exhibited a stable performance; however, vehicle deployment and location clearly affect the routing performance. Based on the findings, it is evident that the Kalman filter- and extended Kalman filter-based location prediction in position-based routing protocol improves the packet delivery ratio, average delay, and throughput, irrespective of the traffic environments and propagation models. Declaration of Competing Interest I, Raj K Jaiswal certify that I have no affiliations with or involvement in any organization orentity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. Supplementary material Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.compeleceng. 2020.106599. References [1] Jaiswal R, Jaidhar C. An applicability of AODV and OLSR protocols on ieee 802.11p for city road in Vanet. In: Internet of Things, Smart spaces, and next generation networks and systems. In: Lecture Notes in Computer Science, 9247. Springer International Publishing; 2015. p. 286–98. doi:10.1007/ 978- 3- 319- 23126- 6_26.

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[2] B K, HT K. Gpsr: greedy perimeter stateless routing for wireless networks. In: Proceeding of mobicom, conference on mobile computing & networking. ACM; 20 0 0. p. 243–54. doi:10.1145/345910.345953. [3] Jerbi M, Meraihi R, Senouci S-M, Ghamri-Doudane Y. Gytar: improved greedy traffic aware routing protocol for vehicular ad hoc networks in city environments. In: Proceedings of the 3rd international workshop on vehicular ad hoc networks. New York, NY, USA: ACM; 2006. p. 88–9. doi:10.1145/ 1161064.1161080. [4] IST-WINNER I. Deliverable 1.1. 2 v. 1.2, winner ii channel modelsǥ, ist-winner2. Tech. Rep. Tech. Rep. 20 08; 20 07. http://projects.celtic-initiative.org/ winner+/deliverables.html. [5] Cheng R-H, Huang C. Efficient prediction-based location updating and destination searching mechanisms for geographic routing in mobile ad hoc networks. J Inf Sci Eng 2012;28(1):115–29. doi:10.6688/JISE.2012.28.1.8. [6] Meghanathan N. Location prediction based routing protocol for mobile ad hoc networks. In: IEEE GLOBECOM 2008 - 2008 IEEE global telecommunications conference; 2008. p. 1–5. doi:10.1109/GLOCOM.2008.ECP.116. [7] Cadger F, Curran K, Santos J, Moffet S. Location and mobility-aware routing for improving multimedia streaming performance in manets. Wirel Pers Commun 2016;86(3):1653–72. doi:10.1007/s11277-015-3012-z. [8] Chegin M, Fathy M. Optimized routing based on mobility prediction in wireless mobile adhoc networks for urban area. In: Fifth international conference on information technology: new generations (ITNG 2008). IEEE; 2008. p. 390–5. doi:10.1109/ITNG.2008.41. [9] Lim MH, Greenhalgh A, Chesterfield J, Crowcroft J. Hybrid routing: a pragmatic approach to mitigating position uncertainty in geo-routing. Tech Rep UCAM-CL-TR-629,ISSN-1476-2986; 2005. [10] Son D, Helmy A, Krishnamachari B. The effect of mobility-induced location errors on geographic routing in mobile ad hoc sensor networks: analysis and improvement using mobility prediction. IEEE Trans Mob Comput 2004;3(3):233–45. doi:10.1109/TMC.2004.28. [11] Vu TK, Kwon S. Mobility-assisted on-demand routing algorithm for manets in the presence of location errors. Sci World J 2014;2014. doi:10.1155/2014/ 790103. [12] Feng H, Liu C, Shu Y, Yang OW. Location prediction of vehicles in Vanets using a Kalman filter. Wirel Pers Commun 2015;80(2):543–59. doi:10.1007/ s11277- 014- 2025- 3. [13] Jaiswal RK, Jaidhar C. Location prediction algorithm for a nonlinear vehicular movement in Vanet using extended Kalman filter. Wirel Netw 2017;23(7):2021–36. doi:10.1007/s11276- 016- 1265- 4. [14] Zhu Y, Jiang R, Yu J, Li Z, Li M. Geographic routing based on predictive locations in vehicular ad hoc networks. EURASIP J Wirel Commun Netw 2014;2014(1):137. doi:10.1186/1687-1499- 2014- 137. [15] Zaki SM, Ngadi M, Kamat M, Razak SA, Shariff JM. A location based prediction service protocol for Vanet city environment. Int J Innov ComputInf Control 2012;8(10):6811–36. [16] Ayaida M, Barhoumi M, Fouchal H, Ghamri-Doudane Y, Afilal L. Phrhls: a movement-prediction-based joint routing and hierarchical location service for Vanets. In: 2013 IEEE international conference on communications (ICC); 2013. p. 1424–8. doi:10.1109/ICC.2013.6654710. [17] Ghafoor H, Koo I. Spectrum-aware geographic routing in cognitive vehicular ad hoc network using a Kalman filter. J Sens 2016;2016. doi:10.1155/2016/ 8572601. [18] Menouar H, Lenardi M, Filali F. Movement prediction-based routing (MOPR) concept for position-based routing in vehicular networks. In: 2007 IEEE 66th vehicular technology conference; 2007. p. 2101–5. doi:10.1109/VETECF.2007.441. [19] Namboodiri V, Gao L. Prediction-based routing for vehicular ad hoc networks. IEEE Trans Veh Technol 2007;56(4):2332–45. doi:10.1109/TVT.2007. 897656. [20] Balico LN, Oliveira HABF, Barreto RS, Loureiro AAF, Pazzi RW. A prediction-based routing algorithm for vehicular ad hoc networks. In: 2015 IEEE symposium on computers and communication (ISCC); 2015. p. 365–70. doi:10.1109/ISCC.2015.7405542. [21] Xue G, Luo Y, Yu J, Li M. A novel vehicular location prediction based on mobility patterns for routing in urban Vanet. EURASIP J Wirel Commun Netw 2012;2012(1):1–14. doi:10.1186/1687-1499- 2012- 222. [22] Li Z, Zhu Y, Li M. Practical location-based routing in vehicular ad hoc networks. In: 2009 IEEE 6th international conference on mobile adhoc and sensor systems; 2009. p. 900–5. doi:10.1109/MOBHOC.2009.5337038. [23] Katsaros K, Dianati M, Tafazolli R, Kernchen R. A novel cross-layer optimized position based routing protocol for Vanets. In: Vehicular networking conference (VNC), 2011 IEEE; 2011. p. 139–46. doi:10.1109/VNC.2011.6117135. [24] Guennebaud G, Jacob B. Eigen v3. http://eigen.tuxfamily.org; 2010. [25] Bavdekar VA, Deshpande AP, Patwardhan SC. Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter. J Process Control 2011;21(4):585–601. doi:10.1016/j.jprocont.2011.01.001. Raj K Jaiswal is working as a Visiting Assistant Professor at the Department of CSIS, BITS Pilani Goa. He has received a Ph.D. degree from the Department of Information Technology, National Institute of Technology Karnataka, Surathkal, India. His research areas of interest include Vehicular Ad hoc Network, SDN/NFV, and Cyber Security.