A Sensing Task Distribution Mechanism For Data Collection Business In Internet Of Vehicles

The traffic management mode under the internet of vehicles (IOV)sets moving vehicles as the main management targets. By analyzing data collected from vehicles and making decisions, the large-scale unified management of vehicles is realized. Among the process, data collection is regarded as the information source of IOV, which is the foundation of vehicular applications. Therefore, the research on data collection oriented task distribution and data fusion in IOV has important theoretical and practical value.However, existing research on data collection oriented task distribution rarely took into account the geographical position and task classification characteristics; as for multi-source data fusion, current research neglected the support degree among several data, and failed to establish a fusion model which is self-adjusted.The main contributions are two folds, which are summarized as follows:(1) To deal with the problem of location-dependent sensing task assignment, we propose a utility-maximization oriented task assignment mechanism for multiple vehicles. First of all, by taking into account the geographical characteristics of both sensing tasks and vehicles, quality requirements of tasks, as well as time budget constraint of owners of vehicles, the mathematical model of multi-vehicle collaborative task assignment problem MVSUM is established, and furthermore, this problem is proved to be NP-hard. Then, this paper proposes an approximation mechanism called LBTA to solve the above problem, a heuristic algorithm composed of two parts: the first part is to determine the allocating order among engaged vehicles by using auction mechanism,and the second part is to schedule optimal sensing path for single vehicle by using an optimal sensing path scheduling (OPS) algorithm to finish this task. At last, we implement the simulation in JAVA and MATLAB programming environment. The simulation results demonstrate correctness and effectiveness of the proposed mechanism.(2) In order to deal with the problem of multi-source sensing data fusioin, and meet the requirements of traffic data fusion for real-time urban traffic state estimation, a new kind of fusion structure model is proposed. This fusion model consists of both spatial fusion and temporal fusion. Firstly, according to the spatial correlation of sensed data collected by multiple detectors, the power average operator is proposed as spatial fusion method. Then according to the temporal correlation of sensed data collected by single detectors, a temporal correlation based data compression (TCDC) algorithm is proposed, which is based on segment linear regression (SLR) algorithm. Extensive simulation results demonstrate the effectiveness and correctness of TCDC algorithm, as well as TCDC's advantage over SLR on overall performance.