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The past eight years have seen the emergence of vehicular clouds as a topic of research in its own right. Vehicular clouds were inspired by the insight that present-day vehicles feature an impressive array of on-board compute, storage and sensing capabilities. These on-board capabilities are a vast untapped resource that, at the moment, is wasted. One of the defining ways in which vehicular clouds differ from conventional clouds is resource volatility. As vehicles enter and leave the cloud, new compute resources become available while others depart, creating a volatile environment where the tasks of enhancing reliability and availability become very challenging. It is intuitively clear that the longer and more predictable the vehicle residency times in the cloud are, the easier it is to ensure reliability and system availability. In this work we look at vehicular clouds with short and unpredictable vehicular residency times. We propose to enhance the reliability and availability of these types of vehicular clouds through a family of redundancy-based job assignment strategies that attempt to mitigate the effect of resource volatility. We offer a theoretical prediction of the Mean Time To Failure (MTTF) of these strategies. We also show how to fine-tune the granularity of the redundancy in order to meet QoS requirements specified in terms of a minimum MTTF for a given user job. Extensive simulations, using vehicle residency data derived from shopping mall statistics, have confirmed the accuracy of our analytical predictions.

By exploiting the ubiquitous nature of Internet of Things (IoT) technology in smart cities, we propose a Multi-Agent Auction-based Parking (MAPark) system for drivers as well as public and private Parking Facility Providers (PFP). In order to improve parking space allocation, the new idea in this paper is to incorporate parking auction with highly influential factors such as type of customer and parking, drivers’ and PFP’s preferences. MAPark leverages real-time demand-based pricing of parking spots, thus charging demand-based price to drivers while providing higher revenue to PFPs. The main contribution of this paper is the introduction of parking auction through sophisticated software agents that are capable of quickly and seamlessly performing tasks such as parking lookup, negotiation, pricing, and reservation. Simulation results show that the proposed scalable and interactive agents of MAPark enables efficient processing of a vast amount of data, providing cost savings in terms of time and fuel for drivers looking for parking, ultimately lowering traffic congestion, and improving usability of public and private parking facilities.