Secure and privacypreserving 3D vehicle positioning schemes for vehicular ad hoc network
Abstract
Industrial wireless networks (IWNs) have applications in areas such as critical infrastructure sectors and manufacturing industries such as car manufacturing. In car manufacturing, IWNs can facilitate manufacturers to improve the design of the vehicles by collecting vehicular status and other related data (such an IWN is also known as vehicular ad hoc networks—VANETs). Vehicle positioning is a key functionality in VANETs. Most existing vehicle positioning systems are capable of providing accurate 2D positioning, but the demand for accurate 3D positioning has increased sharply in recent times (e.g., due to the building of more elevated roads). There are, however, security and privacy concerns relating to 3D positioning systems in VANET. In this paper, we propose two secure and privacypreserving 3D positioning schemes based on vehicletoroadside (V2R) and vehicletovehicle (V2V) communications, respectively. Our schemes are based on the round trip time ranging technique which is used to achieve 3D position. The security and the privacy of vehicles in our schemes are guaranteed through a newly designed onepass authenticated key agreement protocol. Using experiments, we show that a vehicle can determine whether it is on or under an elevated road in a short period of time.
Keywords
Vehicular ad hoc network 3D positioning Security Privacy1 Introduction
Car manufacturing is getting more sophisticated and complex, with more embedded electronics and circuitry (e.g., in smart and driverless vehicles), in today’s competitive landscape. Hence, there is a need for car manufacturers to find ways to achieve efficiencies in their processes, as well as comply with regulatory requirements and be (financially) competitive. Industrial wireless networks (IWNs), such as vehicular ad hoc networks (VANETs), can play an important role in car manufacturing, for example, by facilitating secure collection and dissemination of information to inform decisionmaking [1, 2].
VANETs have been widely studied [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], and there has been renewed focus on such networks due to the increasing popularity of smart cities [23], driverless vehicles [24], intelligent manufacturing [25], and other related Internetconnected systems. A typical VANET mainly consists of vehicles and roadside units (RSUs). A vehicle or RSU may exchange messages with nearby vehicles/RSUs through vehicletovehicle (V2V), vehicletoroadside (V2R), and roadsidetovehicle (R2V) communications, and collectively, V2V, V2R, and R2V are also known as vehicletoeverything (V2X) [26]. Dedicated shortrange communications (DSRC) protocol [27] has been designed to support these communications in VANETs. Through V2X comminations, many VANET applications (e.g., vehicular status data collection, collision warning, speed warning, autonomous navigation, and lane departure alert) [28] can be realized.
Majority of the VANET applications (e.g., secure vehicular status data collection) rely on vehicle positioning, and generally, these positioning systems are based on GPS/BeiDou/GALILEO/GLONASS. Such systems have high horizontal positioning (i.e., 2D positioning) accuracy, but the vertical positioning accuracy is significantly lower due to satellite geometry [29]. As shown in [30], the position accuracy in GPSbased positioning system is approximately 15 m 95% of the time.
Urban traffic environment is becoming more complex, particularly in bigger cities, for example, with the building of more elevated roads to deal with the increasing traffic demands. For example, in North Texas, there are at least 10 interchanges that are 81 ft or taller—and nine of them have been built in roughly the past 15 years^{1}—and as recent as February 2018, the Texas Department of Transportation has proposed building a 7mile elevated freeway to connect Loop 410 to Loop 1604’^{2} in the city of San Antonio. Such elevated roads and highways can pose considerable challenges to existing 2D vehicle positioning systems in VANETs as such systems may not be capable of detecting whether a vehicle is on the elevated road or on the ground road under the elevated road.
In addition to the positioning challenge, one needs to consider security and privacy concerns in a VANET. For example, can we be assured that messages received by vehicles/RSU are from authenticated entities and have not been modified by an active attacker during the transmission? Also, is the driver privacy ensured (e.g., driver’s identity, location, and other sensitive information are not leaked) during the transmission? Without any sound security measures in place, an attacker close to the target vehicle may fabricate information to mislead the vehicle that needs to be positioned, which can have realworld consequences such as a fatal accident. Also, if privacy is not considered, an attacker may easily find the position and identity of the vehicle and exploit this for nefarious intents (e.g., stalking or an attacker may launch a jamming attack that blocks the communications in a destination area).
Cooperative positioning (CP) is one approach that has been used to enhance the accuracy of positioning based on the positionrelated data exchanged among the nodes of a network [31]. Existing CP methods can be broadly categorized into three categories, namely those based on angle of arrival (AOA), those based on radio signal strength (RSS), and those based on distance [32]. AOAbased approaches require large antenna arrays [33], which are not practical for vehicles in a VANET. RSSbased approaches need the knowledge of channel condition and signal transmission power, which may vary over time. Distancebased approaches can be further classified into time of arrival (TOA), time difference of arrival (TDOA), and round trip time (RTT). TOA and TDOA need high precise clock synchronization. RTT only needs to use the timestamps (e.g., signal arrival time and signal sending time) that can be shared among the nodes in a CP system; thus, it is our choice for the proposed scheme in this paper.
While a large number of positioning techniques have been proposed for VANETs in the literature, only a small number of these schemes are also designed for 3D positioning. In [34], for example, the authors showed that a GPS receiver needs at least four satellite signals for a 3D position computation, and these signals are easily blocked. This results in position inaccuracy or unavailability in dense urban environments. In a different work, Alam [35] proposed a CP method for 3D positioning using two satellites. However, this approach suffers from the same inherent issue pertaining to the obstruction of GPS signals. Hossain et al. [36] proposed a GPSfree cooperative vehicular positioning technique that uses TOA data of the reference signal from three RSUs and cooperative vehicles on the road to estimate the position of the candidate vehicle. However, none of these 3D positioning schemes consider security and privacy properties.
In this paper, we propose two secure and privacypreserving 3D positioning schemes for VANETs, which are designed to facilitate the system to determine whether the vehicle is traveling on the elevated road or under the elevated road accurately and efficiently. In our schemes, we use an onepass authenticated key agreement protocol [37], which has been proven to be adequate in establishing a secure channel. In other words, the authentication and vehicle privacy properties are guaranteed through the secure channel.
The first scheme is designed for V2R communications. In the scheme, RSUs and vehicles have to register with a trusted authority (TA), and the TA will generate the required certificates for RSUs and vehicles. When a vehicle that needs to be positioned enters the communication range of a RSU, the RSU broadcasts its certificate to the vehicle. The vehicle receives the certificate and verifies the validity of the certificate. If the certificate is successfully verified, then the RSU and the vehicle execute a key agreement protocol to generate a session key. Finally, the RSU and the vehicle constantly exchange messages for a period of time to achieve the proof of location. The second scheme is designed for V2V communications. A vehicle that needs to be positioned achieves 3D position via a vehicle whose position is known. The concrete steps of this scheme are similar to the first one.
The rest of this paper is organized as follows. In Section 2, we present the system architecture, our design goals, and relevant preliminaries. In Sections 3 and 4, we present our proposed 3D vehicle positioning schemes for V2R communications and V2V communications, respectively. We then evaluate both schemes using simulations. In the evaluation, we adopt the Shadowing model [38] which resembles an actual road scenario. The evaluation and the findings are presented in Section 5. In Section 6, we present our conclusion.
2 Background
2.1 System architecture
 TA is a trusted third party tasked with the generation and publishing of system parameters. TA also issues certificates for vehicles and RSUs in the system.

RSUs are placed along the road, and have embedded processing and communication modules.

Vehicles may drive on an elevated road or on the road below the elevated road. Vehicles also have embedded processing and communication modules, which allow a vehicle to communicate with other nearby vehicle(s) or RSU for cooperative 3D positioning.
2.2 Design goals

Authentication: To guarantee the security of the communications in our schemes, a requester and a responder have to be authenticated by each other. Furthermore, we need to ensure that messages received by the requester/responder have not been modified during the transmission. Finally, a new attack called backdoored pseudorandom generators is exposed [39] recently. If an entity’s chosen random number generator is backdoored, then an attacker may violate the security of a scheme. We require that if a backdoored pseudorandom generator is selected, then the authentication property still holds.

Vehicle privacy: In a 3D vehicle positioning scheme, entities other than the requester and the responder should not learn the real identities and positions of the participating vehicles.

Low error rate: A requester may determine its 3D position with overwhelming success rate and within a reasonable time frame (i.e. short delay).
2.3 Distance Function
In our schemes, we have to calculate the distance between two entities. We first consider the 2D condition. Suppose the position coordinates of the two entities are L_{1}=(x_{1},y_{1}) and L_{2}=(x_{2},y_{2}), where x_{i} and y_{i} are the longitude and latitude of a entity’s position, i∈{1,2}. Generally, a positioning system has some errors. Here, we assume the 2D positioning accuracy of each entity is ε.
2.4 Nonce generator and hedged extractor
The nonce generator (NG) and hedged extractor (HE) are used to generate the randomness required to mitigate backdoored pseudorandom generators.
The algorithm NG takes as input the security parameter 1^{k}, a current state St, and a nonce selector ω, and returns a nonce n_{0} belonging to the range set {0,1}^{∗} of NG as well as an updated state St^{′} through NG(1^{k},ω,St)→(n_{0},St^{′}). HE takes a seed xk∈{0,1}^{∗}, a message m, and a nonce n_{0} to deterministically return a string r=HE(xk,(m,n_{0})) [40].
3 Proposed secure and privacypreserving 3D vehicle positioning for V2R communications
In this section, we present our first proposed scheme, which requires that a vehicle that intends to run a CP protocol to connect to an RSU directly. We also assume that a vehicle may learn its 2D coordinates, the 2D positioning accuracy is Δ m, and the height of elevated road is h m.
The scheme consists of four stages, namely setup, registration, secure channel establishment, and secure location proof. In the first stage, the TA generates the system parameters and master key. The vehicles and RSUs are enrolled by the TA in the second stage. In the third stage, a secure channel is established between a vehicle and an RSU. The secure channel is used to protect the privacy of the vehicle. In the fourth stage, the vehicle and the RSU run a V2R CP protocol for secure positioning.
3.1 Setup
 1.
Selects a cyclic multiplicative group \(\mathbb {G}\) with prime order q and chooses a generator \(g\in \mathbb {G}\).
 2.
Generates a master key s and the corresponding master public key g_{0}. The master key will be used by the TA to issue certificates for the vehicles and RSUs in the system. The master public key will be used to verify the validity of the certificates.
 3.
Chooses a symmetric encryption scheme \(\mathcal {E}_{K_{1}}(\cdot)/\mathcal {D}_{K_{1}}(\cdot)\), and a MAC scheme \(\mathcal {M}_{K_{2}}\). For simplicity, we assume the symmetric key K_{1} used in the symmetric encryption scheme and the symmetric key K_{2} used in the MAC scheme have the same length.
 4.
Chooses a hash function H:{0,1}^{∗}→{0,1}^{l}, where l is the bitlength of the symmetric key used in \(\mathcal {E}_{K}(\cdot)/\mathcal {D}_{K}(\cdot)\) and \(\mathcal {M}_{K}\).
 5.
Publishes \(\text {pub}=\left (\mathbb {G}, q, H, g_{0}, g, \mathcal {E}_{K_{1}}(\cdot)/\mathcal {D}_{K_{1}}(\cdot), \mathcal {M}_{K_{2}}\right)\) as the system parameters.
3.2 Registration
The vehicles and RSUs have to be enrolled by the TA.
For vehicle \(\mathcal {V}_{i}\), it runs the nonce generator NG to generate a nonce n_{i} and a next state \(St^{\prime }_{i}\) through \(\phantom {\dot {i}\!}\left (n_{i}, St^{\prime }_{i}\right)\leftarrow \text {NG}(\mu _{i}, {St}_{i})\) with a current state St_{i} and a nonce selector μ_{i} and generates a randomness vs_{i} used in the next step with hedged extractor HE though vs_{i}←HE(xk_{i},(m_{i},n_{i})). Then, it computes \( {vp}_{i}=g^{{vs}_{i}}\), (vp_{i},vs_{i}) to be used as the public/private key pair of \(\mathcal {V}_{i}\). TA also issues an anonymous certificate \(\text {cert}_{\mathcal {V}_{i}}\) for \(\mathcal {V}_{i}\). To further enhance the privacy of a vehicle, the vehicle may ask TA to issue a pool of anonymous certificates for itself. Each anonymous certificate is only for shortterm usage.
3.3 Secure channel establishment
We assume an RSU will periodically broadcast its certificates in its communication range and the altitude of the RSU is higher than that of the elevated road. Assume a vehicle \(\mathcal {V}_{i}\) wants to run a CP protocol to learn its 3D position and it may connect to the RSU \(\mathcal {R}_{j}\) directly. In this stage, a secure channel between R_{j} and V_{i} is established.
We note that, from \(\text {cert}_{\mathcal {R}_{j}}\), \(\mathcal {V}_{i}\) may learn the position information of R_{j}.
The session key used to establish a secure channel is \(sk=k_{\mathcal {V}_{i}, \mathcal {R}_{j}}=k_{\mathcal {R}_{j}, \mathcal {V}_{i}}\).
3.4 Secure location proof
 1.
Sends its current timestamp \({ts}_{\mathcal {R}_{j} \eta }\) to \(\mathcal {V}_{i}\), where η is initially set to 1.
 2.
Generates a MAC \(\text {MAC}_{\mathcal {R}_{j} \eta }=\mathcal {M}_{sk}\left ({ts}_{\mathcal {R}_{j} \eta },\eta \right)\) and sends \(\text {MAC}_{\mathcal {R}_{j} \eta }\) to \(\mathcal {V}_{i}\).
 1.
Sends \({ts}_{\mathcal {V}_{i}\eta }\) to \(\mathcal {R}_{j}\).
 2.
On receiving \(\text {MAC}_{\mathcal {R}_{j\eta }}\), checks \(\text {MAC}_{\mathcal {R}_{j\eta }}\stackrel {?}{=}\mathcal {M}_{sk}\left ({ts}_{\mathcal {R}_{j\eta }},\eta \right)\). If the equation holds, then proceeds to the next substep; otherwise, aborts.
 3.
Generates a ciphertext \(C_{\mathcal {V}_{i} \eta }=\mathcal {E}_{sk} \left (L_{\mathcal {V}_{i}\eta }\right)\) and a MAC \(\text {MAC}_{\mathcal {V}_{i} \eta }=\mathcal {M}_{sk}\left (C_{\mathcal {V}_{i} \eta },{ts}_{\mathcal {V}_{i}\eta },\eta \right)\).
 4.
Sends \(\left (C_{\mathcal {V}_{i} \eta }, \text {MAC}_{\mathcal {V}_{i} \eta }\right)\) to \(\mathcal {R}_{j}\).
 1.
On receiving \(\left (C_{\mathcal {V}_{i} \eta }, \text {MAC}_{\mathcal {V}_{i} \eta }\right)\), verifies the validity of \(\text {MAC}_{\mathcal {V}_{i} \eta }\) by checking \(\text {MAC}_{\mathcal {V}_{i} \eta }\stackrel {?}{=}\mathcal {M}_{sk}(C_{\mathcal {V}_{i} \eta },{ts}_{\mathcal {V}_{i}\eta }, \eta)\). If the equation holds, then computes \(L_{\mathcal {V}_{i}\eta }=\mathcal {D}_{sk} (C_{\mathcal {V}_{i} \eta });\) otherwise, aborts.
 2.
Sets η=η+1 and proceeds to step 1.
Once phase 1 has successfully concluded, \(\mathcal {V}_{i}\) may calculate its 3D position information in phase 2. We note that, for round n, steps 2 and 3 are used to acknowledge that \(\mathcal {V}_{i}\) has received the messages broadcasted by \(\mathcal {R}_{j}\) in step 1. Assume the final session round is n and 2D positioning accuracy is Δ m.
 1.Calculates the theoretical distance range \([{ls}_{\text {min}, \mathcal {V}_{i}\eta }, {ls}_{\text {max}, \mathcal {V}_{i} \eta }]\) in the case \(\mathcal {V}_{i}\) is on the elevated road, where$$\begin{array}{@{}rcl@{}} {ls}_{\text{min}, \mathcal{V}_{i} \eta}=\sqrt{Df\left(L_{\mathcal{V}_{i}\eta}, L_{\mathcal{R}_{j}}, \Delta\right)^{2}+\left(z_{\mathcal{R}_{j}}h\right)^{2}} \end{array} $$(5)$$\begin{array}{@{}rcl@{}} {ls}_{\text{max}, \mathcal{V}_{i} \eta}=\sqrt{Df\left(L_{\mathcal{V}_{i}\eta}, L_{\mathcal{R}_{j}}, +\Delta\right)^{2}+\left(z_{\mathcal{R}_{j}}h\right)^{2}} \end{array} $$(6)Computes the accumulated theoretical distance range$$\begin{array}{@{}rcl@{}}\left[{Ls}_{\text{min}, \mathcal{V}_{i}},{Ls}_{\text{max}, \mathcal{V}_{i}}\right]=\left[\sum\limits_{\eta=1}^{n1}{{ls}_{\text{min}, \mathcal{V}_{i} \eta}},\sum\limits_{\eta=1}^{n1}{{ls}_{\text{max}, \mathcal{V}_{i} \eta}}\right] \end{array} $$(7)
 2.Calculates the theoretical distance range \([{ld}_{\text {min}, \mathcal {V}_{i} \eta }, {ld}_{\text {max}, \mathcal {V}_{i} \eta }]\) in the case \(\mathcal {V}_{i}\) is under the elevated road, where$$\begin{array}{@{}rcl@{}}{ld}_{\text{min}, \mathcal{V}_{i} \eta}=\sqrt{Df \left(L_{\mathcal{V}_{i} \eta}, L_{\mathcal{R}_{j}}, \Delta\right)^{2}+ z_{\mathcal{R}_{j}}^{2}} \end{array} $$(8)$$\begin{array}{@{}rcl@{}}{ld}_{\text{max}, \mathcal{V}_{i} \eta}=\sqrt{Df\left(L_{\mathcal{V}_{i} \eta}, L_{\mathcal{R}_{j}}, +\Delta\right)^{2}+ z_{\mathcal{R}_{j}}^{2}} \end{array} $$(9)Computes the accumulated theoretical distance range$$\begin{array}{@{}rcl@{}} \left[{Ld}_{\text{min}, \mathcal{V}_{i}},{Ld}_{\text{max}, \mathcal{V}_{i}}\right]=\left[\sum\limits_{\eta=1}^{n1}{{ld}_{\text{min}, \mathcal{V}_{i} \eta}},\sum\limits_{\eta=1}^{n1}{{ld}_{\text{max}, \mathcal{V}_{i} \eta}}\right] \end{array} $$(10)
 3.Calculates the computational distance \(s_{\mathcal {V}_{i} \eta }\) between \(\mathcal {V}_{i}\) and \(\mathcal {R}_{j}\), where$$\begin{array}{@{}rcl@{}}s_{\mathcal{V}_{i} \eta}=C \times \left[\left({ts}_{\mathcal{V}_{i} \eta+1}{ts}_{\mathcal{V}_{i} \eta}\right)/2\right] \end{array} $$(11)Computes the accumulated computational distance$$\begin{array}{@{}rcl@{}}S_{\mathcal{V}_{i}}=\sum\limits_{\eta=1}^{n1}{s_{\mathcal{V}_{i} \eta}} \end{array} $$(12)
 4.
By using \(S_{\mathcal {V}_{i}}\), \(\left [{Ls}_{\text {min}, \mathcal {V}_{i}},{Ls}_{\text {max}, \mathcal {V}_{i}}\right ]\) and \(\left [{Ld}_{\text {min}, \mathcal {V}_{i}},{Ld}_{\text {max}, \mathcal {V}_{i}}\right ]\), we can calculate \({Po}_{\mathcal {V}_{i}}\) and \({Pu}_{\mathcal {V}_{i}}\), where \({Po}_{\mathcal {V}_{i}}\) is the probability that \(\mathcal {V}_{i}\) is on the elevated road and \({Pu}_{\mathcal {V}_{i}}\) is the probability that \(\mathcal {V}_{i}\) is not on the elevated road.
There are two cases where we calculate the probability. (a)
The first is that there is no overlap between \(\left [{Ls}_{\text {min}, \mathcal {V}_{i}},{Ls}_{\text {max}, \mathcal {V}_{i}}\right ]\) and \(\left [{Ld}_{\text {min}, \mathcal {V}_{i}},{Ld}_{\text {max}, \mathcal {V}_{i}}\right ]\). If \(S_{\mathcal {V}_{i}}\) is in the range of \(\left [{Ls}_{\text {min}, \mathcal {V}_{i}},{Ls}_{\text {max}, \mathcal {V}_{i}}\right ]\) or on the left side of \(\left [{Ls}_{\text {min}, \mathcal {V}_{i}},{Ls}_{\text {max}, \mathcal {V}_{i}}\right ]\), then \({Po}_{\mathcal {V}_{i}}=1\); if \(S_{\mathcal {V}_{i}}\) is in the range of \(\left [{Ld}_{\text {min}, \mathcal {V}_{i}},{Ld}_{\text {max}, \mathcal {V}_{i}}\right ]\) or on the right side of \(\left [{Ld}_{\text {min}, \mathcal {V}_{i}},{Ld}_{\text {max}, \mathcal {V}_{i}}\right ]\), then \({Pu}_{\mathcal {V}_{i}}=1\). If \(S_{\mathcal {V}_{i}}\) is not within the overlap between the \(\left [{Ls}_{\text {min}, \mathcal {V}_{i}},{Ls}_{\text {max}, \mathcal {V}_{i}}\right ]\) and \(\left [{Ld}_{\text {min}, \mathcal {V}_{i}},{Ld}_{\text {max}, \mathcal {V}_{i}}\right ]\), then we will take the principle of proximity to calculate the probability.
 (b)
The second is that there is overlap between \(\left [{Ls}_{\text {min}, \mathcal {V}_{i}},{Ls}_{\text {max}, \mathcal {V}_{i}}\right ]\) and \(\left [{Ld}_{\text {min}, \mathcal {V}_{i}},{Ld}_{\text {max}, \mathcal {V}_{i}}\right ]\). If the \(S_{\mathcal {V}_{i}}\) is in the range of \(\left [{Ls}_{\text {min}, \mathcal {V}_{i}},{Ls}_{\text {max}, \mathcal {V}_{i}}\right ]\) but not in the overlapping range or on the left side of \(\left [{Ls}_{\text {min}, \mathcal {V}_{i}},{Ls}_{\text {max}, \mathcal {V}_{i}}\right ]\), then \({Po}_{\mathcal {V}_{i}}=1\); if the \(S_{\mathcal {V}_{i}}\) in the \(\left [{Ld}_{\text {min}, \mathcal {V}_{i}},{Ld}_{\text {max}, \mathcal {V}_{i}}\right ]\) but not in the overlapping range or the right side of the \(\left [{Ld}_{\text {min}, \mathcal {V}_{i}},{Ld}_{\text {max}, \mathcal {V}_{i}}\right ]\), then \({Pu}_{\mathcal {V}_{i}}=1\); if the \(S_{\mathcal {V}_{i}}\) is in the range of overlap, then using the same principle of proximity to calculate the probability.
The concrete calculation is as follows:
1. If \({Ls}_{\text {max}, \mathcal {V}_{i}}\leq {Ld}_{\text {min}, \mathcal {V}_{i}}\) (a)
If \(S_{\mathcal {V}_{i}}\leq {Ls}_{\text {max}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=1\), \({Pu}_{\mathcal {V}_{i}}=0\).
 (b)
If \(S_{\mathcal {V}_{i}}\geq {Ld}_{\text {min}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=0\), \({Pu}_{\mathcal {V}_{i}}=1\).
 (c)
If \(S_{\mathcal {V}_{i}}>{Ls}_{\text {max}, \mathcal {V}_{i}}\) and \(S_{\mathcal {V}_{i}}<{Ld}_{\text {min}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=\frac {{Ld}_{\text {min}, \mathcal {V}_{i}}S_{\mathcal {V}_{i}}}{{Ld}_{\text {min}, \mathcal {V}_{i}}{Ls}_{\text {max}, \mathcal {V}_{i}}}\), \({Pu}_{\mathcal {V}_{i}}=\frac {S_{\mathcal {V}_{i}}{Ls}_{\text {max}, \mathcal {V}_{i}}}{{Ld}_{\text {min}, \mathcal {V}_{i}}{Ls}_{\text {max}, \mathcal {V}_{i}}}\).
2. If \({Ls}_{\text {min}, \mathcal {V}_{i}}<{Ld}_{\text {min}, \mathcal {V}_{i}}<{Ls}_{\text {max}, \mathcal {V}_{i}}<{Ld}_{\text {max}, \mathcal {V}_{i}}\) (a)
If \(S_{\mathcal {V}_{i}}\leq {Ld}_{\text {min}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=1\), \({Pu}_{\mathcal {V}_{i}}=0\).
 (b)
If \(S_{\mathcal {V}_{i}}\geq {Ls}_{\text {max}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=0\), \({Pu}_{\mathcal {V}_{i}}=1\).
 (c)
If \(S_{\mathcal {V}_{i}}>{Ld}_{\text {min}, \mathcal {V}_{i}}\) and \(S_{\mathcal {V}_{i}}<{Ls}_{\text {max}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=\frac {{Ls}_{\text {max}, \mathcal {V}_{i}}S_{\mathcal {V}_{i}}}{{Ls}_{\text {max}, \mathcal {V}_{i}}{Ld}_{\text {min}, \mathcal {V}_{i}}}\), \({Pu}_{\mathcal {V}_{i}}=\frac {S_{\mathcal {V}_{i}}{Ld}_{\text {min}, \mathcal {V}_{i}}}{{Ls}_{\text {max}, \mathcal {V}_{i}}{Ld}_{\text {min}, \mathcal {V}_{i}}}\).
3. If \({Po}_{\mathcal {V}_{i}}> {Pu}_{\mathcal {V}_{i}}\), then it can be determined that \(\mathcal {V}_{i}\) is on the elevated road. If \({Po}_{\mathcal {V}_{i}}< {Pu}_{\mathcal {V}_{i}}\), then it can be determined that \(\mathcal {V}_{i}\) is not on the elevated road. Therefore, the height of \(\mathcal {V}_{i}\) can be determined.
 (a)
3.5 Security analysis
In the secure channel establishment stage, a secure channel is established between \(\mathcal {V}_{i}\) and \(\mathcal {R}_{j}\). Essentially, it is an anonymous onepass authenticated key agreement protocol. A onepass authenticated key agreement protocol requires only one entity to send a message to another entity. The secure channel can be established with a short delay. The onepass authenticated key agreement protocol used in this paper is derived from the protocol in [37], which has been proven to be secure. Furthermore, in the onepass authenticated key agreement protocol, the anonymous certificate is used to hide \(\mathcal {V}_{i}\)’s identity, and the position information of \(\mathcal {V}_{i}\) is sent through the secure channel. In addition, in the key agreement protocol, we use a nonce generator NG and a hedged extractor HE to generate the randomness. Thus, as shown in [40], this anonymous onepass authenticated key agreement protocol is secured against backdoored pseudorandom generators. Therefore, the authentication and vehicle privacy properties are guaranteed.
4 Proposed secure and privacypreserving 3D vehicle positioning for V2V communications
In the scheme introduced in the preceding section, the vehicles achieve 3D positioning via RSUs. However, RSUs have limited deployment and coverage. Therefore, we propose a 3D vehicle positioning scheme for V2V communications in this section.
The scheme also consists of four stages, namely setup, registration, secure channel establishment, and secure location proof. In the first stage, TA generates the system parameters and master key. The vehicles are enrolled by the TA in the second stage. In the third stage, a secure channel is established between a vehicle with a known 3D position and a vehicle with an unknown 3D position. The secure channel is used to protect the privacy of the two vehicles. In the last stage, the two vehicles run a V2V CP protocol for secure positioning. We assume that the vehicle with unknown 3D location can only obtain the exact 2D coordinates, and the height of elevated road is h m.
The setup and registration stages are the same as those in the previous scheme. Thus, in the following, we only introduce secure channel establishment and secure location proof stages.
4.1 Secure channel establishment
Assume a vehicle \(\mathcal {V}_{i}\) wishes to execute a V2V CP protocol to learn its 3D position, and it may connect to a vehicle \(\mathcal {V}_{j}\) with a known 3D position directly. At this stage, a secure channel between \(\mathcal {V}_{j}\) and \(\mathcal {V}_{i}\) is established.
The session key used to establish a secure channel is \(sk=k_{\mathcal {V}_{i}, \mathcal {V}_{j}}=k_{\mathcal {V}_{j}, \mathcal {V}_{i}}\).
4.2 Location proof
 1.
Sends \({ts}_{\mathcal {V}_{j} \zeta }\) to \(\mathcal {V}_{i}\).
 2.
Generates a ciphertext \(C_{\mathcal {V}_{j} \zeta }=\mathcal {E}_{sk} \left (L_{\mathcal {V}_{j} \zeta }\right)\) and a MAC \(\text {MAC}_{\mathcal {V}_{j} \zeta }=\mathcal {M}_{sk}\left (C_{\mathcal {V}_{j} \zeta },{ts}_{\mathcal {V}_{j} \zeta }, \zeta \right)\).
 3.
Sends \(\left (C_{\mathcal {V}_{j} \zeta }, \text {MAC}_{\mathcal {V}_{j} \zeta }\right)\) to \(\mathcal {V}_{i}\).
 1.
Sends \({ts}_{\mathcal {V}_{i} \zeta }\) to \(\mathcal {V}_{j}\).
 2.
On receiving \(\text {MAC}_{\mathcal {V}_{j \zeta }}\), checks \(\text {MAC}_{\mathcal {V}_{j} \zeta }\stackrel {?}{=}\mathcal {M}_{sk}\left (C_{\mathcal {V}_{j} \zeta },{ts}_{\mathcal {V}_{j} \zeta }, \zeta \right)\). If the equation holds, then computes \(L_{\mathcal {V}_{j} \zeta }=\mathcal {D}_{sk} \left (C_{\mathcal {V}_{j} \zeta }\right)\) and proceeds to the next substep; otherwise, aborts.
 3.
Generates a ciphertext \(C_{\mathcal {V}_{i} \zeta }=\mathcal {E}_{sk} \left (L_{\mathcal {V}_{i} \zeta }\right)\) and a MAC \(\text {MAC}_{\mathcal {V}_{i} \zeta }=\mathcal {M}_{sk}\left (C_{\mathcal {V}_{i} \zeta },{ts}_{\mathcal {V}_{i} \zeta }, \zeta \right)\).
 4.
Sends \(\left (C_{\mathcal {V}_{i} \zeta }, \text {MAC}_{\mathcal {V}_{i} \zeta }\right)\) to \(\mathcal {V}_{j}\).
 1.
On receiving \(\left (C_{\mathcal {V}_{i} \zeta }, \text {MAC}_{\mathcal {V}_{i} \zeta }\right)\), verifies the validity of \(\text {MAC}_{\mathcal {V}_{i} \zeta }\) by checking \(\text {MAC}_{\mathcal {V}_{i} \zeta }\stackrel {?}{=}\mathcal {M}_{sk}\left (C_{\mathcal {V}_{i} \zeta },{ts}_{\mathcal {V}_{i} \zeta }, \zeta \right)\). If the equation holds, then computes \(L_{\mathcal {V}_{i} \zeta }=\mathcal {D}_{sk} \left (C_{\mathcal {V}_{i} \zeta }\right);\) otherwise, aborts.
 2.
Sets ζ=ζ+1 and proceeds to step 1.
Once phase 1 is successfully finished, \(\mathcal {V}_{i}\) may calculate its 3D position information in phase 2. Assume the final session round is n and 2D positioning accuracy is Δ m.
 1.Calculates the theoretical distance range \(\left [{ls}_{\text {min}, \mathcal {V}_{i} \zeta }, {ls}_{\text {max}, \mathcal {V}_{i} \zeta }\right ]\) in the case \(\mathcal {V}_{i}\) and \(\mathcal {V}_{j}\) are at the same layer, where$$\begin{array}{@{}rcl@{}} {ls}_{\text{min}, \mathcal{V}_{i} \zeta}=Df^{\prime}\left(L_{\mathcal{V}_{i}, {ts}_{\mathcal{V}_{i} \zeta}}, L_{\mathcal{V}_{j}, {ts}_{\mathcal{V}_{j} \zeta}}, 2\Delta\right) \end{array} $$(15)$$\begin{array}{@{}rcl@{}}{ls}_{\text{max}, \mathcal{V}_{i}, \zeta}=Df^{\prime}\left(L_{\mathcal{V}_{i}, {ts}_{\mathcal{V}_{i} \zeta}}, L_{\mathcal{V}_{j}, {ts}_{\mathcal{V}_{j} \zeta}}, +2\Delta\right) \end{array} $$(16)Computes the accumulated theoretical distance range$$\begin{array}{@{}rcl@{}} [{Ls}_{\text{min}, \mathcal{V}_{i}},{Ls}_{\text{max}, \mathcal{V}_{i}}]=\left[\sum\limits_{\zeta=1}^{n1}{{ls}_{\text{min}, \mathcal{V}_{i} \zeta}},\sum\limits_{\zeta=1}^{n1}{{ls}_{\text{max}, \mathcal{V}_{i} \zeta}}\right] \end{array} $$(17)
 2.Calculates the theoretical distance range \(\left [{ld}_{\text {min}, \mathcal {V}_{i} \zeta }, {ld}_{\text {max}, \mathcal {V}_{i} \zeta }\right ]\) in the case \(\mathcal {V}_{i}\) and \(\mathcal {V}_{j}\) are at different layers, where$$\begin{array}{@{}rcl@{}} {ld}_{\text{min}, \mathcal{V}_{i} \zeta}=\sqrt{{ls}_{\text{min}, \mathcal{V}_{i} \zeta}^{2}+h^{2}} \end{array} $$(18)$$\begin{array}{@{}rcl@{}} {ld}_{\text{max}, \mathcal{V}_{i} \zeta}=\sqrt{{ls}_{\text{max}, \mathcal{V}_{i} \zeta}^{2}+h^{2}} \end{array} $$(19)Computes the accumulated theoretical distance range$$\begin{array}{@{}rcl@{}} \left[{Ld}_{\text{min}, \mathcal{V}_{i}},{Ld}_{\text{max}, \mathcal{V}_{i}}\right]\,=\,\left[\sum\limits_{\zeta=1}^{n1}{{ld}_{\text{min}, \mathcal{V}_{i} \zeta}},\sum\limits_{\zeta=1}^{n1}{{ld}_{\text{max}, \mathcal{V}_{i} \zeta}}\right] \end{array} $$(20)
 3.Calculates the computational distance \(s_{\mathcal {V}_{i} \zeta }\) between \(\mathcal {V}_{i}\) and \(\mathcal {V}_{j}\), where$$\begin{array}{@{}rcl@{}} s_{\mathcal{V}_{i} \zeta}=C\times\left[\left({ts}_{\mathcal{V}_{i} \zeta+1}{ts}_{\mathcal{V}_{i} \zeta}\right)/2\right] \end{array} $$(21)Computes the accumulated computational distance$$\begin{array}{@{}rcl@{}} S_{\mathcal{V}_{i}}=\sum\limits_{\zeta=1}^{n1}{s_{\mathcal{V}_{i} \zeta}} \end{array} $$(22)
 4.
By using \(S_{\mathcal {V}_{i}}\), \(\left [{Ls}_{\text {min}, \mathcal {V}_{i}},{Ls}_{\text {max}, \mathcal {V}_{i}}\right ]\) and \(\left [{Ld}_{\text {min}, \mathcal {V}_{i}},{Ld}_{max, \mathcal {V}_{i}}\right ]\), we can calculate \({Po}_{\mathcal {V}_{i}}\) and \({Pu}_{\mathcal {V}_{i}}\), where \({Po}_{\mathcal {V}_{i}}\) is the probability that \(\mathcal {V}_{i}\) is on the elevated road and \({Pu}_{\mathcal {V}_{i}}\) is the probability that \(\mathcal {V}_{i}\) is not on the elevated road. The calculation is as follows:
1. If \({Ls}_{\text {max}, \mathcal {V}_{i}}\leq {Ld}_{\text {min}, \mathcal {V}_{i}}\) (a)
If \(S_{\mathcal {V}_{i}}\leq {Ls}_{\text {max}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=1\), \({Pu}_{\mathcal {V}_{i}}=0\).
 (b)
If \(S_{\mathcal {V}_{i}}\geq {Ld}_{\text {min}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=0\), \({Pu}_{\mathcal {V}_{i}}=1\).
 (c)
If \(S_{\mathcal {V}_{i}}>{Ls}_{\text {max}, \mathcal {V}_{i}}\) and \(S_{\mathcal {V}_{i}}<{Ld}_{\text {min}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=\frac {{Ld}_{\text {min}, \mathcal {V}_{i}}S_{\mathcal {V}_{i}}}{{Ld}_{\text {min}, \mathcal {V}_{i}}{Ls}_{\text {max}, \mathcal {V}_{i}}}\), \({Pu}_{\mathcal {V}_{i}}=\frac {S_{\mathcal {V}_{i}}{Ls}_{\text {max}, \mathcal {V}_{i}}}{{Ld}_{\text {min}, \mathcal {V}_{i}}{Ls}_{\text {max}, \mathcal {V}_{i}}}\).
2. If \({Ls}_{\text {min}, \mathcal {V}_{i}}<{Ld}_{\text {min}, \mathcal {V}_{i}}<{Ls}_{\text {max}, \mathcal {V}_{i}}<{Ld}_{\text {max}, \mathcal {V}_{i}}\) (a)
If \(S_{\mathcal {V}_{i}}\leq {Ld}_{\text {min}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=1\), \({Pu}_{\mathcal {V}_{i}}=0\).
 (b)
If \(S_{\mathcal {V}_{i}}\geq {Ls}_{\text {max}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=0\), \({Pu}_{\mathcal {V}_{i}}=1\).
 (c)
If \(S_{\mathcal {V}_{i}}>{Ld}_{\text {min}, \mathcal {V}_{i}}\) and \(S_{\mathcal {V}_{i}}<{Ls}_{\text {max}, \mathcal {V}_{i}}\), \({Po}_{\mathcal {V}_{i}}=\frac {{Ls}_{\text {max}, \mathcal {V}_{i}}S_{\mathcal {V}_{i}}}{{Ls}_{\text {max}, \mathcal {V}_{i}}{Ld}_{\text {min}, \mathcal {V}_{i}}}\), \({Pu}_{\mathcal {V}_{i}}=\frac {S_{\mathcal {V}_{i}}{Ld}_{\text {min}, \mathcal {V}_{i}}}{{Ls}_{\text {max}, \mathcal {V}_{i}}{Ld}_{\text {min}, \mathcal {V}_{i}}}\).
3. If \({Po}_{\mathcal {V}_{i}}> {Pu}_{\mathcal {V}_{i}}\), it can be determined that \(\mathcal {V}_{i}\) and \(\mathcal {V}_{j}\) are in the same layer. If \({Po}_{\mathcal {V}_{i}}< {Pu}_{\mathcal {V}_{i}}\), then it can be determined that \(\mathcal {V}_{i}\) and \(\mathcal {V}_{j}\) are in different layers. Therefore, the height of \(\mathcal {V}_{i}\) can be determined.
 (a)
4.3 Security analysis
In the secure channel establishment stage, a secure channel is established between \(\mathcal {V}_{i}\) and \(\mathcal {V}_{j}\). Essentially, it is an anonymous onepass authenticated key agreement protocol which is the same as that of the previous scheme. Furthermore, in the onepass authenticated key agreement protocol, anonymous certificates are used to hide the identity of \(\mathcal {V}_{i}\) and \(\mathcal {V}_{j}\) and the position information of \(\mathcal {V}_{i}\) and \(\mathcal {V}_{j}\) is sent through the secure channel. In addition, in the key agreement protocol, we use a nonce generator NG and a hedged extractor HE to generate the randomness. Thus, this anonymous onepass authenticated key agreement protocol is secured against backdoored pseudorandom generators. This also ensures the authentication and vehicle privacy properties.
5 Simulation
In this section, we evaluate the performance of our schemes using NS2.35, which is an open source network communication simulator. In NS2.35, there are three types of propagation models: free space, tworay ground model, and shadowing model [38]. In the first two models, the distance is the only variable parameter in the simulation, but the introduction of random events in the shadowing model is closer to the realworld scenario. So in our experiment, we chose shadowing model as our experimental model.
The purpose of our scheme is to accurately determine the position of the vehicle, so we need to find an appropriate time segment Δt to ensure the correct rate of positioning. Our experiments were implemented on a Linux machine using an Intel Core i74790 at frequency of 3.60 GHz. In our experiments, the channel bandwidth bound was 6 Mbps. The vehicle’s speed ranged from 30 to 60 km/h, and the average speed was 50 km/h. The communication radius of an RSU and each vehicle was set to be 300 m. For each experiment, the simulation time was about 20 s. Therefore, we can think of the vehicle’s speed remaining constant during the simulations.
5.1 3D positioning based on V2R communications
5.2 3D positioning based on V2V communications
In this section, a vehicle that needs to be positioned achieves 3D position via a vehicle whose position is known. This case is more complex than the previous case. In the previous case, the RSU is fixed and only the vehicle is moving. However in this case, two vehicles are moving simultaneously. So we have to consider the traveling direction of the two vehicles: opposite direction, facetoface, and same direction.
6 Conclusion
As smart vehicles become more commonplace and smart cities being the norm, ensuring accurate 3D vehicle positioning schemes (and potentially 4D and beyond in the foreseeable future) while also ensuring the security and privacy of the data and the participating vehicles will be increasingly important.
In this paper, we proposed two secure and privacypreserving 3D positioning schemes for V2R and V2V communications in a VANET. We demonstrated the utility of both schemes using simulations, in the sense that the schemes achieve their design goals (i.e., 3D positioning of the vehicles) within an acceptable time frame without compromising the security and privacy of the data and the participating vehicles.
Future research includes extending the proposed schemes to consider multiple vehicles and other types of vehicles such as unmanned aerial vehicles (also known as drones).
Footnotes
 1.
http://www.startelegram.com/news/traffic/yourcommute/article122735029.html, last accessed May 13, 2018.
 2.
https://www.mysanantonio.com/news/local/article/BanderaRoadsfuturetreelinedboulevard12558886.php, last accessed May 13, 2018.
Notes
Acknowledgements
This work is supported by the National Key R&D program of China (No. 2017YFB0802000); NSF of China (Nos. 61572198, 61502237); and the Fundamental Research Funds for the Central Universities.
Authors’ contributions
All authors contribute to the design and evaluation of the schemes and the writing of the manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
 1.L. Zhang, Q. Wu, J. DomingoFerrer, B. Qin, C. Hu, Distributed aggregate privacypreserving authentication in vanets. IEEE T. Intell. Transp. Syst.18(3), 516–526 (2017).CrossRefGoogle Scholar
 2.S. Latif, S. Mahfooz, N. Ahmad, B. Jan, H. Farman, M. Khan, K. Han, Industrial internet of things based efficient and reliable data dissemination solution for vehicular ad hoc networks. Wirel. Commun. Mob. Com.2018:, 1–16 (2018).CrossRefGoogle Scholar
 3.C. Wu, Y. Ji, F. Liu, S. Ohzahata, T. Kato, Toward practical and intelligent routing in vehicular ad hoc networks. IEEE T. Veh. Technol.64(12), 5503–5519 (2015).CrossRefGoogle Scholar
 4.L. Zhang, Q. Wu, A. Solanas, J. DomingoFerrer, A scalable robust authentication protocol for secure vehicular communications. IEEE T. Veh. Technol.59(4), 1606–1617 (2010).CrossRefGoogle Scholar
 5.K. Abboud, W. Zhuang, Stochastic modeling of singlehop cluster stability in vehicular ad hoc networks. IEEE T. Veh. Technol.65(1), 226–240 (2016).CrossRefGoogle Scholar
 6.L. Zhang, Q. Wu, B. Qin, J. DomingoFerrer, B. Liu, Practical secure and privacypreserving scheme for valueadded applications in VANETs. Comput. Commun.71:, 50–60 (2015).CrossRefGoogle Scholar
 7.L. Zhang, OTIBAAGKA: a new security tool for cryptographic mixzone establishment in vehicular ad hoc networks. IEEE T. Inf. Foren. Sec.12(12), 2998–3010 (2017).CrossRefGoogle Scholar
 8.X. Wei, W. Chen, B. Chen, B. Chen, B. Chen, et al, Bspline wavelet on interval finite element method for static and vibration analysis of stiffened flexible thin plate. CMCComput. Mater. Con.52(1), 53–71 (2016).Google Scholar
 9.C. Wu, E. Zapevalova, Y. Chen, F. Li, Time optimization of multiple knowledge transfers in the big data environment. CMCComput. Mater. Con.54(3), 269–285 (2018).Google Scholar
 10.L. Zhang, X. Meng, K. K. R. Choo, Y. Zhang, F. Dai, Privacypreserving cloud establishment and data dissemination scheme for vehicular cloud. IEEE T. Depend. Secure. 1:, 1–1 (2018). https://doi.org/10.1109/TDSC.2018.2797190.Google Scholar
 11.J. Liu, J. Li, L. Zhang, F. Dai, Y. Zhang, X. Meng, J. Shen, Secure intelligent traffic light control using fog computing. Future Gener. Comp. Sy.78:, 817–824 (2018).CrossRefGoogle Scholar
 12.Z. Xia, N. N. Xiong, A. V. Vasilakos, X. Sun, Epcbir: An efficient and privacypreserving contentbased image retrieval scheme in cloud computing. Inform. Sci.387:, 195–204 (2017).CrossRefGoogle Scholar
 13.Z. Xia, X. Ma, Z. Shen, X. Sun, N. N. Xiong, B. Jeon, Secure image LBP feature extraction in cloudbased smart campus. IEEE Access. 6:, 30392–30401 (2018).CrossRefGoogle Scholar
 14.L. Zhang, C. Hu, Q. Wu, J. DomingoFerrer, B. Qin, Privacypreserving vehicular communication authentication with hierarchical aggregation and fast response. IEEE Trans. Comput.65(8), 2562–2574 (2016).MathSciNetCrossRefGoogle Scholar
 15.J. Cui, Y. Zhang, Z. Cai, A. Liu, Y. Li, Securing display path for securitysensitive applications on mobile devices. CMC Comput. Mater. Contin. 55:, 17–35 (2018).Google Scholar
 16.H. Cheng, Z. Su, N. Xiong, Y. Xiao, Energyefficient node scheduling algorithms for wireless sensor networks using Markov Random Field model. Inform. Sci.329:, 461–477 (2016).CrossRefGoogle Scholar
 17.H. Cheng, D. Feng, X. Shi, C. Chen, Data quality analysis and cleaning strategy for wireless sensor networks. EURASIP J. Wirel. Comm.2018(1), 61 (2018).CrossRefGoogle Scholar
 18.B. Lin, W. Guo, N. Xiong, G. Chen, A. V. Vasilakos, H. Zhang, A pretreatment workflow scheduling approach for big data applications in multicloud environments. IEEE Trans. Netw. Serv.13(3), 581–594 (2016).CrossRefGoogle Scholar
 19.H. Zheng, W. Guo, N. Xiong, A kernelbased compressive sensing approach for mobile data gathering in wireless sensor network systems. IEEE T. Syst. Man Cy.S. (2017).Google Scholar
 20.N. Xiong, J. He, J. H. Park, D. H. Cooley, Y. Li, A neural network based vehicle classification system for pervasive smart road security. J. Univers. Comput. Sci.15(5), 1119–1142 (2009).Google Scholar
 21.A. Shahzad, R. Landry, M. Lee, N. Xiong, J. Lee, C. Lee, A new cellular architecture for information retrieval from sensor networks through embedded service and security protocols. Sensors. 16(6), 821 (2016).CrossRefGoogle Scholar
 22.L. Zhang, J. Li, Enabling robust and privacypreserving resource allocation in fog computing. IEEE Access. 6:, 50384–50393 (2018). https://doi.org/10.1109/ACCESS.2018.2868920.CrossRefGoogle Scholar
 23.L. Wu, Y. Zhang, K. K. R. Choo, D. He, Efficient identitybased encryption scheme with equality test in smart city. IEEE Trans. Sustain. Comput.3(1), 44–55 (2018).CrossRefGoogle Scholar
 24.G. De La Torre, P. Rad, K. K. R. Choo, Driverless vehicle security: challenges and future research opportunities. Future Gener. Comp. Sy. (2018). https://doi.org/10.1016/j.future.2017.12.041.
 25.X. Li, D. Li, J. Wan, A. V. Vasilakos, C. F. Lai, S. Wang, A review of industrial wireless networks in the context of industry 4.0. Wirel. Netw.23(1), 23–41 (2017).CrossRefGoogle Scholar
 26.Y. Yang, Z. Wei, Y. Zhang, H. Lu, K. K. R. Choo, H. Cai, V2x security: A case study of anonymous authentication. Pervasive Mob. Comput.41:, 259–269 (2017).CrossRefGoogle Scholar
 27.R. Bera, J. Bera, S. Sil, S. Dogra, N. B. Sinha, D. Mondal, in Wireless and Optical Communications Networks, 2006 IFIP International Conference On. Dedicated short range communications (DSRC) for intelligent transport system (IEEEBangalore, 2006), pp. 1–5.Google Scholar
 28.R. Kroh, A. Kung, F. Kargl, Sevecom deliverable 1.1, version 2.0: Vanets security requirements final version. Technical report, Technical report, 6th Framework Programme (2006).Google Scholar
 29.A. HasnurRabiain, A. Kealy, N. Alam, A. Dempster, C. Toth, D. Brzezinska, V. Gikas, C. Danezis, G. Retscher, in Proceedings of the PACIFIC Positioning, Navigation and Timing of the Institute of Navigation (ION PNT 2013). Cooperative positioning using GPS, lowcost INS and dedicated short range communications, (2013), pp. 769–779.Google Scholar
 30.O. Ringdahl, T. Hellström, I. Wästerlund, O. Lindroos, Estimating wheel slip for a forest machine using RTKDGPS. J. Terrramech.49(5), 271–279 (2012).CrossRefGoogle Scholar
 31.N. Alam, A. G. Dempster, Cooperative positioning for vehicular networks: facts and future. IEEE T. Intell. Transp.14(4), 1708–1717 (2013).CrossRefGoogle Scholar
 32.G. Mao, Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking: Monitoring and Surveillance Techniques for Target Tracking (IGI Global, New York, 2009).Google Scholar
 33.A. Yassin, Y. Nasser, M. Awad, A. AlDubai, R. Liu, C. Yuen, R. Raulefs, E. Aboutanios, Recent advances in indoor localization: a survey on theoretical approaches and applications. IEEE Commun. Surv. Tut.19(2), 1327–1346 (2016).CrossRefGoogle Scholar
 34.A. Boukerche, H. A. Oliveira, E. F. Nakamura, A. A. Loureiro, Vehicular ad hoc networks: a new challenge for localizationbased systems. Comput. commun.31(12), 2838–2849 (2008).CrossRefGoogle Scholar
 35.N. Alam, Vehicular positioning enhancement using DSRC. PhD thesis (University of New South Wales, Sydney, Australia, 2012). http://unsworks.unsw.edu.au/fapi/datastream/unsworks:10502/SOURCE02?view=true.Google Scholar
 36.M. A. Hossain, I. Elshafiey, A. AlSanie, in Applied Electromagnetics (APACE), 2016 IEEE AsiaPacific Conference On. Cooperative vehicular positioning with VANET in urban environments (IEEE, 2016), pp. 393–396.Google Scholar
 37.L. Zhang, Certificateless onepass and twoparty authenticated key agreement protocol and its extensions. Inform. Sci.293:, 182–195 (2015).CrossRefGoogle Scholar
 38.I. Gruber, O. Knauf, H. Li, in Proceedings of European Wireless. Performance of ad hoc routing protocols in urban environments (Barcelona, 2004), pp. 24–27.Google Scholar
 39.Y. Dodis, C. Ganesh, A. Golovnev, A. Juels, T. Ristenpart, in Annual International Conference on the Theory and Applications of Cryptographic Techniques. A formal treatment of backdoored pseudorandom generators (SpringerHeidelberg, 2015), pp. 101–126.zbMATHGoogle Scholar
 40.M. Bellare, B. Tackmann, in Annual International Conference on the Theory and Applications of Cryptographic Techniques. Noncebased cryptography: retaining security when randomness fails (SpringerHeidelberg, 2016), pp. 729–757.Google Scholar
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