Full Length ArticleAn efficient key distribution system for data fusion in V2X heterogeneous networks
Introduction
Data fusion in Cooperative Intelligent Transport Systems (C-ITS) is truly compelling to make transportation safer and more efficient in recent years. The data based applications deployed in C-ITS are promising to deeply change people’s driving experience by reducing traffic congestion, providing intelligent transportation algorithms, reducing significantly the number of traffic accidents, and finally realizing unmanned vehicles [1].
With the development of hardware and software, data can be collected and fused on different C-ITS components. We are in the big data era for transportation based communication systems. Smart modules, as shown in Fig. 1, such as vehicle cameras, sensors, and smart control systems are deployed on vehicles for data collection purpose. Thus, data collected from these modules both in physical environments and cyber systems is increasing rapidly in both volume and number of types [2]. On the other hand, improvement of data fusion and analytic algorithms especially artificial intelligent algorithms have become more and more efficient to meet the real-time data fusion needs in C-ITS networks.
Another important and fundamental technology for data fusion in C-ITS networks is the wireless communication between vehicles and other components in C-ITS networks. The two main categories of wireless communication technologies for V2X communication systems are IEEE 802.11p standard based [3] and cellular based [4]. For the IEEE 802.11p standard based communication technology, there are Dedicated Short-Range Communications (DSRC) standards in US [3] and Intelligent Transportation System (ITS)-G5 standards in Europe [3]. In fact, IEEE 802.11p can meet most V2X application requirement with the most stringent performance specifications. However, the cellular based communication technology such as LTE, 5G [4] can also be used as another option to build direct and stable network links such as for communication between vehicles and remote servers [4]. In Fig. 1, an example is given to illustrate the different communication technologies are deployed as a heterogeneous network for data exchange and data fusion based decision makings.
For data exchange in this heterogeneous communication network, concept of Vehicle-to-Everything (V2X) communication has been introduced and classified according to the communication parties [5]: Vehicle-to-Vehicle (V2V), Vehicle-to-Pedestrian (V2P), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Network (V2N). Basically, this concept is used to sort the collected data in the application layer further for the data fusion based decision making systems on one vehicle. Among the four types of V2X communications, V2V and V2I are the most promising technologies for data exchange used for future C-ITS networks [5].
Data fusion and data analytics based on data collected from onboard sensors (cameras, sensors, radars, and control systems) and received from the heterogeneous V2X networks can derive high-level intelligent decision makings. In turn, these intelligent decisions made on each vehicle can then be sent to other components in C-ITS network for improving the traffic system. For drivers, cooperative data fusion can provide a better vision of an intersection and help detect other vehicles or pedestrians that the driver would overlook due to complex driving environments like obstacles, distraction, or bad weather. In the future, data fusion based applications on V2X heterogeneous networks will help to realize the unmanned vehicles on roads.
With the introduction of data fusion in V2X communication, data exchange between vehicles and other components in C-ITS networks is complicated as different data formats are transmitted through heterogeneous networks. Some of these data types are related to driving and safety applications, especially in an unmanned driving environment. Thus, one important issue is, based on one vehicle’s viewpoint, whether the data received from the V2X networks, can be trusted especially it is used for safety-related decision makings. For example, an attacker might compromise the transmitting system to intentionally send false or wrong data to compromise the whole C-ITS network. Also, data transmitted in the V2X network might be changed by an attacker to mislead vehicles which may lead to traffic accidents. Privacy may also be violated as eavesdropping and illegal tracking may happen as an attacker can receive the periodical broadcasting Cooperative Awareness Messages (CAM) [6] containing vehicles’ geographical position, speed, and some sensitive information.
Countermeasures like security and privacy protection schemes are introduced to build the trust system for data exchanged in V2X networks. Research works such as [7] have deployed a Public Key Infrastructure (PKI) to distribute temporary keys for providing security and privacy for data exchange in V2X networks. However, extra latency is introduced by security schemes such as key distribution and other security related calculations such as algorithms providing data source authentication, data content integrity, and data encryption. V2X is naturally not expected to have high latency toleration as safety-related applications require ultra-low latency (e.g. pre-crash sensing warning normally require a total latency of less than 50 ms [4]). Thus, building an efficient data fusion trust system for data exchanged in the V2X heterogeneous networks is the fundamental step for further data fusion in C-ITS.
In this article, our main contribution is to propose an efficient data fusion trust system including (1) a location-based key distribution scheme that can highly reduce latency due to current key distributions; (2) an acceleration computing solution by fully deploying the vehicles’ onboard GPUs for security related calculations.
We organize this paper following the order below. Section 2 gives an example of the specific question we are going to solve. Section 3 illustrates our proposed location-based key distribution as well as several details. Next, Section 4 shows the parallel computing model for accelerating security related calculations. Furthermore, Section 5 shows performance evaluation results for the proposed data fusion trust system. Section 6 presents a brief discussion and future works. Finally, Section 7 draws the conclusion of this study.
Section snippets
Problem definition
As mentioned in Section 1, from viewpoints of vehicles, data fusion in a V2X heterogeneous network must consider the issue that whether the data received through V2X networks can be trusted. Previous works [8] have demonstrated the related concepts of trust of data fusion in V2X networks. However, a general data fusion trust system architecture is still lacked. In this section, we first define four trust levels for the data fusion trust system which, to the best of our knowledge, is the first
A location-based key distribution system
In this section, we present a location-based key distribution system with pre-calculated and pre-distributed key pair pools for vehicles and RSUs. In this scheme, updating the temporary keys is not based on time period but based on the locations of the vehicles. For one time period (e.g. 24 h), temporary keys (ATs) are pre-distributed and downloaded to vehicles and RSUs. As long as one vehicle moves to one zone, the temporary keys will be updated according to its location. Thus, the updating
GPU accelerated security calculations
The latency in the V2X communication systems can also be increased as the security related calculations are introduced as shown in Section 2. In this section, we first list all security related calculations based on the level of trust we designed. Then a GPU based design will then be presented to give an evaluated performance compared with existing solutions.
Performance evaluation
In this section, the evaluation of performance is presented for our data fusion trust model. We simulate the heterogeneous network of V2X communications on NS-3 simulator [27] including short-range communication network between vehicles and RSUs and Internet protocols between RSUs and the key distribution center. Average latency due to key distribution is calculated for the location-based key distribution system with different prediction success ratio.
Discussions and future work
In this paper, we introduced the four level trust model for the data transmission on the V2X heterogeneous network. In fact, the key distribution process can also be seen as one of the data transmission process which must be deployed with the highest trust level. We hope for future work, a more general data transmission trust model for data fusion in V2X can be presented with including key distribution.
We claimed that a key distribution system such as a PKI is necessary for implementing the
Conclusions
In this article, we presented a data fusion trust system for V2X heterogeneous networks. Firstly a definition with four levels of data fusion trust is given according to V2X application needs. Then a location-based key distribution scheme is designed to efficiently distribute keys that build a foundation for all levels of data trust implementations for data fusion in V2X networks. Security related calculations are also considered and accelerated by GPGPU for data exchange including both secure
Acknowledgement
This work has been partially conducted in SCOOP, a franco-european pilot project for connected vehicles and roads under EC Grant No.2014-EU-TA-0669-S and INEA/CEF/TRAN/A2014/1042281.
This work is also supported by National Natural Science Foundation of China under Grant No.61873309, No.61572137, and No.61728202, and Shanghai 2018 Innovation Action Plan project under Grant No.18510760200-Research on smart city big data processing technology based on cloud-fog mixed mode.
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