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In order to reduce the number and total distance of changing parking spaces in a parking lot or a given parking area through autonomous valet parking (AVP), in doing so to lower the related cost and the potential accident risk, this paper formula ted a vehicle-slot matching optimization model which satisfied the shared parking requirement. In view of the feature of autonomous vehicle freely translocating during parking, this paper defined the match, match slice and match map corresponding to decision variable and feasible solution by subdividing the shared parking demand and supply in time. By conception replacement, the paper changed the searching effective neighbor of match map into the classic assignment problem and solved it with Hungarian algorithm. In view of the NP-hard feature of the matching model, this paper proposed a designed tabu searching algorithm. Numerical analysis not only verified the rationality of this model and the effectiveness of the algorithm, also proved that the model and algorithm could deal with the shared parking of conventional vehicles. The results show that AVP can improve the utility of shared parking berths and increase the number of served vehicles. [ABSTRACT FROM AUTHOR]

In order to minimize the average completion time of a series of geo-distributed data analytical jobs, this paper first introduced online random scheduling algorithm ranT A. ranT A actually showed the preference on the task assignment under the consideration of both computing capacity of edges and the network bandwidth. Furthermore, in order to avoid overloading those edges with low computing capacities, this paper proposed data redistribution mechanism ranT A-data based on ranT A by redistributing some data to the central data center along with the tasks. The result of ranT A -data could be proved concentrated on its optimum with high probability. Extensive simulations show that ranTA-data improves nearly 30% performance compared with traditional algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the problem of channel and power allocation in centralized cognitive networks, this paper proposed a case-based reasoning and improved heuristically accelerated Q-learning algorithm. In order to improve the convergence speed of the Q-learning algorithm, it matched the current allocation problem with the stored historical case, and selected the Q value of the most similar case, which was normalized as the initial value of the heuristically accelerated Q-learning algorithm. In order to improve the performance of the heuristically accelerated Q-learning, it introduced a guidance function based on information intensity to change the action strategy by emphasizing the importance of the action; it considered energy efficiency in the design of the reward function. The simulation results show that the proposed algorithm can significantly improve the system energy efficiency and convergence speed, which has carried the channel and power allocation. [ABSTRACT FROM AUTHOR]