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## Vehicle-assisted framework for delay-sensitive applications in smart cities

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With the advancement in technology, applications such as real-time object detection, route predictions, and infotainment required a significant amount of computing power. However, resource constraint onboard computing units installed in vehicles cannot provide the desired computing. Therefore, complex computation tasks are offloaded to nearby vehicles, connected roadside units (RSUs), or data centers. However, the main objective is to optimize the use of resources and to minimize the communication delay. Thus, various techniques have been proposed to support the computation of complex applications through near real-time resource sharing. In this chapter, we summarize the recent contributions proposed to support task distribution over vehicular networks. This work provides an overview of vehicular-assisted frameworks and their challenges. Further, explored a various resource selection methods proposed in the literature for efficient task offloading. To provide better understanding, the existing techniques are categorized as traditional, game-theory, fuzzy, and reward-based. This chapter serves as a guide for researchers to understand the challenges of vehicular-assisted networks and state-of-the-art contributions.

Chapter Contents:

• 8.1 Introduction
• 8.2 Vehicular networks
• 8.3 Vehicle-assisted network and their challenges
• 8.4.1 Emerging decision models for vehicular networks
• 8.4.2 Data protection, security, and trust management
• 8.5 Applications of vehicular networks
• 8.6 Conclusion
• 8.7 Future directions
• References

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