Vehicular Cloud Computing (VCC): Application Trends and Challenges in a New Paradigm

1. INTRODUCTION

 Cloud computing is a new trend in Information Technology (IT) and networking. It improves management, development and deliverance of computing services, being a safe approach towards data storage and sharing, allowing businesses to access software on the internet as service. Security and privacy of data are needed to be considered in this area of IT.

Vehicular Cloud Computing (VCC) is a new, recent paradigm that shifts from the conventional Vehicular Ad hoc Network (VANET), using the vehicular cloud to attain resources. According to Iftikhar Ahmad [2], VCC “has changed vehicular communication and underlying traffic management applications”. Mario Gerla [5] described VCC as a specific form of mobile cloud computing merging with VANET to have vehicle-to-vehicle data exchange.Essentially, it is the sharing of vehicle-related data and resources between vehicles and related infrastructures.

 The automotive industry gradually moves towards development of fully autonomous vehicles. Requirements for vehicle data storage grows rapidly as technology improves. For instance, traffic patterns and real-time location updates, are generated every moment a vehicle operates. Hypothetically, it can require more than a few laptops to store data for a day of driving. Vehicle networking has become a research area of significance. Vehicles are now expected to carry more communication systems, increased sensing power, and other features that lead to more intelligent and efficient automobiles. Hence, VCC became one of the known solutions addressing vehicular network challenges.

 VCC is now a hot topic in the automotive industry. This literature review will examine several interesting applications; identifying issues and challenges in terms of resources, privacy and security; and discovering possible solutions to overcome the challenges and have robust road security and intelligence in traffic management.

1.1. FEATURES OF VEHICULAR CLOUD COMPUTING (VCC)

 VCC has a distinguishing feature that separates itself from conventional cloud computing when it comes to the resources provided. It has volatility of the availability of service providers due to high vehicle mobility, making it useful to tackle unplanned scenarios that can occur at any time and place.

VCC focuses on exploiting under-utilised vehicular resources to help come to possible solutions for recurring vehicular network and traffic-related challenges. This emphasises on the accelerated adoption of VANETs, consisting of applications that centre their focus on safety and efficiency. For instance, VCC is used to support assessment and analysis of traffic conditions, consequently responding and showing alerts more appropriately on-the-go.

1.2. TAXONOMY OF VEHICULAR CLOUDS

Vehicular clouds are formed to provide vehicular-based and on-board resources and services respectively, improving mobility experience, adaptability and route planning. There are different cloud types for VCC, classified into two main areas: (1) Vehicle-to-Vehicle (V2V) and (2) Vehicle-to-Infrastructure (V2I) clouds [3, 7].

V2V clouds revolve around the infrastructure of communication between vehicles. V2V clouds are used by vehicles to exchange data about each other’s statuses, resulting in intelligent and quick decision-making towards route planning. Vehicles have sensors installed receiving vehicle-related data and can share these with other vehicles. [3] Consider a scenario where a vehicle, approaching an intersection, can use V2V clouds to send queries to cameras of a vehicle nearer to the intersection, subsequently capturing a real-time image of the intersection [2]. This is useful towards advanced route planning and avoiding accidents by the intersection with other vehicles.

V2I clouds incorporates vehicles with infrastructural communication networks, for instance WIFI and 3G/LTE. This is where traffic-monitoring sensors help form a cloud, sometimes named as “roadside sensor clouds”, according to the research article in [3]. This allows vehicles to have improved cooperative communication and sensing technologies for greater transport safety.

2. VEHICULAR CLOUD COMPUTING (VCC) APPLICATIONS

 Several application scenarios will be discussed here, especially popular ones in terms of aiding the automotive industry and society.

2.1. Traffic Management

According to the Global Status Report on Road Safety 2018 by the World Health Organisation (WHO), “The number of road traffic deaths continues to rise steadily, reaching 1.35 million in 2016” [12]. Hence, many organisations have considered the implementation of Vehicular Cloud Computing on Traffic Management Systems (TMS). The increase in global population increases demand for vehicular transportation. Therefore, traffic management plays a vital role on the road to find best solutions to tackle situations on the road.

Dynamic Traffic Light Management is one way of traffic management, which provides the adequate resources from participating vehicles in the traffic to autonomously find a solution without waiting for official responses. According to eBooks in [5, 6, 10, 11], VANETs and Intelligent Transportation Systems (ITSs) are not capable of reporting traffic situations efficiently without VCC support to report GPS position snapshots and traffic information. This eases exchange of traffic-related data between vehicles on from a large distance, therefore still able to react appropriately.

Most of these system signals happen offline, meaning there is no flexible adaptation towards uncertain changes in traffic condition. VCC utilizes vehicular networks to improve system signals to respond accordingly to situations on the road. This is known as Traffic Signal Optimization. Mario Gerla [5] emphasized that VCC provides prior knowledge of malfunctioning traffic lights, thus commencing immediate detour decisions rather than awaiting Navigation Server orders in that of vehicles. Hence, VCC can help support vehicles to pool resources and have dynamic responses to avoid traffic accidents.

However, limitations include having a small geographical coverage, which can be further exploited in future works to cover its weaknesses.

2.2. Managing Evacuation

 Response towards emergency situations is crucial as subsequent outcomes can be catastrophic. VCC enables tackling of such events by forming vehicular clouds that will participate in the evacuation procedure. VCC utilizes V2V and V2I clouds for gathering information on time, location and availability of resources, whilst forming vehicular clouds with the rescue response team, who uploads updated information on survival necessities to the main server. This supports the search of safe and fast transport routes for emergency evacuations from the main road. Md Whaiduzzaman [11] states that, prior to implementing evacuation plans, transportation agencies develop simulations for possible evacuation events, identifying potential traffic control strategies. Transportation agencies can then combat anomalies and ensure greater road safety during evacuation procedures.

 Kayhan Zrar Ghafoor [6] describes another scenario of an evacuation event, where evacuation zones can be selected with VCC appropriately based on travelling time and resource availability, then shared amongst vehicles in the participating vehicular clouds. However, there is conflicting evidence on this. Md Whaiduzzaman [11] addresses that evacuations concern mostly on irregular events, so advanced notices cannot be made. Therefore, there can be no sustainable planning and event responses without numerous anomalies based on assumptions, especially for large scale events.

 Tackling this, the development of a system architecture with three layers, such as the cloud infrastructure as a service, intelligent layer, and the system interface, was done by Mehmood and Nekovee (2007) and Schweiger (2011) [9]. This system uses data analysis to process data and find an optimum solution, also managing road traffic through the control and coordination of infrastructure available. This system was evaluated and proved to have effective improvements in tackling disaster events and managing evacuations.

 Kayhan Zrar Ghafoor [6] explained a research concerning a smart traffic cloud capable of outsourcing traffic sensing data to build digitised maps with real-time traffic updates and conditions. Authors added that it is “quite significant to many emergency events, e.g., evacuation after a disaster…”. This is useful and in support of tackling the drawbacks of advanced notices stated in [11].

2.3. Formation of Data Centres

 People tend to spend hours in various places, for instance airports and shopping malls. Vehicles are then be parked for long hours, remaining idle with under-utilized communication resources. VCC applies the act of exploiting such resources to form a vehicular cloud, promoting content sharing.

 There is, however, conflicting evidence towards how this strategy benefits in the long run. Consider long-term parking lots filled with vehicles at a point of time. Whaiduzzaman [11] emphasises on the idea of companies giving employees incentives for renting resources to form a vehicular cloud, hence becoming a data centre in the area (p.335). In terms of shopping malls, the eBook have also claimed that there have been studies showing that 95% of teenage shoppers spend more than an hour in the shopping mall, while 68% spend more than two hours [11]. Essentially, shop management can provide incentives for customers to share resources of their parked vehicles to form vehicular clouds, and again, create a data centre at the mall as a form of VCC application.

 However, to have data centres active for a long period of time is dependent on the time limit that vehicles can be parked in an area for.

3. SECURITY AND PRIVACY CHALLENGES

Being different from traditional cloud computing, VCC requires sophisticated mechanisms for security and privacy protection. Here, security and privacy issues will be addressed and in response to all applications in a security, privacy and resource perspective.

3.1. Threats to Resources

Resources are a vital part of forming the whole vehicular cloud transmission of data between vehicles and infrastructures. It can be an issue for cases of allocating accurate amounts of resources rather than pre-allocating them.

Mohamed Eltoweissy [4] explained that traffic congestions are events that occur daily, therefore the dynamic nature of the issue is not feasible to solve consistently. Additionally, solutions require heavy computational resources and effort, meaning larger workforces and time needed in development (p.7). This conveys mismanagement in resource consumption due to improper timing of when to fully utilise resources. Eltoweissy mentioned, however, that the problem is easier to solve when events occur in an “on-demand fashion”, thus having the right amount of resources being allocated rather than setting pre-allocated resources based on the worst-case scenarios.

3.2. Security and Privacy Challenges and Solutions of Vehicular Cloud Messages

Farhan Ahmad [1] explained that messages contain crucial information of an event, normally exchanged between vehicles in V2V and V2I communication. It is inevitable that threats will always exist due to an attacker’s interest in compromising the confidentiality, integrity and availability of such messages (CIA Triad).

A solution to this can include the use of cryptographic tools, which provide several security techniques, including confidentiality, integrity and availability. Cryptography involves the use of encryption-decryption algorithms, and Meijri [9] has proposed cryptographic-based solutions to the following issues below.

Illegal monitoring of message transmissions in vehicular clouds is a major threat that can compromise confidentiality of messages. Mohamed Nidhal Mejriand Shaym Gumaste [8, 9] highlighted that attackers attain information on location of vehicles and the routes that they have taken to achieve confidentiality exploitation. This tempers with the privacy of individuals and other passengers in vehicles and rectifying this may be difficult due to its passiveness in the virtual realm. A possible solution can be encrypting data of high priority, such as data on a vehicle’s identity and location.

  Integrity of messages involves the protection against improper modification or its destruction, ensuring authenticity of data transmission across vehicular clouds. Threats for compromising message integrity normally occurs within V2V communications compared to V2I ones, as V2V clouds are much more fragile and easier for attackers to infiltrate. An attack example can include broadcast tempering [9]. Cryptographic primitives can be enabled to prevent this, given that the attack is done by an authorized node in the network [9].

Availability of messages can be compromised through Denial of Service (DoS) attacks. Mejri [9] explained how an attacker would attempt to disrupt communications and interrupt services simply by flooding vehicular cloud control channels with high volumes of messages until the network is unable to handle data amount it received. Another example can be a timing attack, where an attacker attempts to compromise a cryptosystem by analyzing execution timings for the cryptographic algorithm. The solution would be a timestamping mechanism for packets of applications that are delay-sensitive [9].

4. CONCLUSION

All sources highlighted VCC as a fresh paradigm, still from reaching its limits on changing lives on the road. VCC allows the sharing of resources between vehicles to coordinate best planned routes for passenger safety and travel efficiency, thus making intelligent decisions. In this document, the taxonomy of vehicular clouds; several application scenarios; security and privacy issues and its solutions; and key strategies for management have been discussed and identified. The 2 main classes in the taxonomy of vehicular clouds were also discussed.

However, VCC applications will always obstacles and anomalies to handle and overcome. It is noteworthy that new expansion in research is required to create more VCC reference models, protocols and architectures as the issues behind security and privacy will never stop evolving. As supported by several sources, issues and threats can simply never be eradicated completely, but there are always more innovative and effective solutions to keep the after effects at a minimum.

Bibliography:

1. Ahmad, F., Kazim, M. and Adnane, A. (2015). Vehicular Cloud Networks: Architecture, Applications and Security Issues. [ebook] Limassol, Cyprus: IEEE, pp.572-576. Available at: https://ieeexplore.ieee.org/document/7431476 [Accessed 5 Feb. 2019].
2. Ahmad, I., Md. Noor, R., Ali, I. and Ahsan Qureshi, M. (2017). The Role of Vehicular Cloud Computing in Road Traffic Management: A Survey. [ebook] pp.1-9. Available at: https://www.researchgate.net/publication/312444489_The_Role_of_Vehicular_Cloud_Computing_in_Road_Traffic_Management_A_Survey [Accessed 6 Feb. 2019].
3. Ahmad, I., Noor, R., Ali, I., Imran, M. and Vasilakos, A. (2017). Characterizing the role of vehicular cloud computing in road traffic management. [ebook] Wei Yu. Available at: https://journals.sagepub.com/doi/full/10.1177/1550147717708728 [Accessed 2 Feb. 2019].
4. Eltoweissy, M., Olariu, S. and Younis, M. (2010). Towards Autonomous Vehicular Clouds. [ebook] Available at: https://pdfs.semanticscholar.org/8a66/0bd54eff945257e606baf3731051904eda8f.pdf [Accessed 7 Feb. 2019].
5. Gerla, M. (2012). Vehicular Cloud Computing. [ebook] IEEE, pp.152-155. Available at: http://nrlweb.cs.ucla.edu/nrlweb/publication/download/779/06257116_1_.pdf [Accessed 6 Feb. 2019].
6. Ghafoor, K., Mohammed, M., Bakar, K., Sadiq, A. and Lloret, J. (2015). Vehicular Cloud Computing: Trends and Challenges. [ebook] pp.1-7. Available at: https://www.researchgate.net/publication/262773787_Vehicular_Cloud_Computing_Trends_and_Challenges [Accessed 4 Feb. 2019].
7. Gu, L., Zeng, D. and Guo, S. (2013). Vehicular cloud computing: A survey. [ebook] Available at: https://www.researchgate.net/publication/269304545_Vehicular_cloud_computing_A_survey [Accessed 5 Feb. 2019].
8. Gumaste, S. and K., R. (2015). Security Challenges Addressed in Vehicular cloud computing. [ebook] Shaym Gumaste, pp.1961-1964. Available at: https://www.researchgate.net/publication/281092048_Security_Challenges_Addressed_in_Vehicular_cloud_computing [Accessed 11 Feb. 2019].
9. Mejri, M., Ben-Othman, J. and Hamdi, M. (2014). Survey on VANET security challenges and possible cryptographic solutions. [ebook] pp.54 -66. Available at: https://ac.els-cdn.com/S2214209614000187/1-s2.0-S2214209614000187-main.pdf?_tid=dd23384b-c861-4ea5-b721-56cf1075a774&acdnat=1549317751_d82a1bf50abcc321a43e3a79224d5144 [Accessed 5 Feb. 2019].
10. Saleem, J., Jamil, N., Naseem, M., Jahan, A. and Javed, S. (n.d.). Vehicular Cloud Computing ( VCC ). [ebook] International Journal of Computer Science Engineering (IJCSE). Available at: http://www.ijcse.net/docs/IJCSE18-07-04-033.pdf [Accessed 6 Feb. 2019].
11. Whaiduzzaman, M., Sookhak, M., Gani, A. and Buyya, R. (2013). A survey on vehicular cloud computing. [ebook] pp.334-340 Available at: http://www.cloudbus.org/papers/Vehicular-Cloud.pdf [Accessed 5 Feb. 2019].
12. World Health Organisation (WHO) (2018). Global Status Report on Road Safety. [online] World Health Organisation (WHO), p.1. Available at: https://apps.who.int/iris/bitstream/handle/10665/277370/WHO-NMH-NVI-18.20-eng.pdf?ua=1 [Accessed 6 Feb. 2019].