PROJECT TITLE :
Distributed Social Welfare Maximization in Urban Vehicular Participatory Sensing Systems - 2018
We have a tendency to think about the crucial downside of maximizing the social welfare of a vehicular participatory sensing system, where the system's social welfare is measured by the number of sensing information delivered to a central platform through a vehicular unintended network. The key to the problem is to manage network stability since both network congestion and idleness can slump system social welfare. However, several great challenges exist. Initial, restricted vehicle-to-vehicle (V2V) link capacity and vehicle buffer size will lead to heavy network congestion when every individual vehicle blindly injects an excessive amount of information into the network hoping to get additional rewards. Second, the highly dynamic network topology and stochastic inter-vehicle contacts have a heavy impact on the performance of multi-hop knowledge transmission. Third, vehicles need to be practically rewarded primarily based on their sensing and transmission cost, that, however, greatly vary among vehicles. To tackle the aforementioned challenges, we have a tendency to propose a distributed backpressure control approach, the primary work to the simplest of our information, to maximize the social welfare while balancing network stability for a vehicular participatory sensing system. Combining vehicular network properties and Lyapunov optimization techniques, individualized methods are developed for each participant to manage its sensing rate, create its own routing choices, and set its own value for information relaying. Formally proved by rigorous theoretical analysis, the social welfare achieved by the proposed approach is comparative to the optimum performance. In addition, intensive knowledge-driven simulations primarily based on real taxi GPS traces are conducted, and also the results confirm the efficacy of the proposed algorithm.
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