To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side infrastructure. The scheme enables fined-grained partial raw sensing data selection, transmission, fusion, and processing in per-object granularity, by exploiting the parallelism among object classification subtasks associated with each object. A supervised learning model is trained to capture the relationship between the object classification accuracy and the data quality of selected object sensing data, facilitating accuracy-aware sensing data selection. We formulate an optimization problem for joint sensing data selection, subtask placement and resource allocation among multiple object classification subtasks, to minimize the total resource cost while satisfying the delay and accuracy requirements. A genetic algorithm based iterative solution is proposed for the optimization problem. Simulation results demonstrate the accuracy awareness and resource efficiency achieved by the proposed cooperative sensing and computing scheme, in comparison with benchmark solutions.

Online multi-kernel learning is promising in the era of mobile computing, in which a combined classifier with multiple kernels are offline trained, and online adapts to personalized features for serving the end user precisely and smartly. The online adaptation is mainly carried out at the end-devices, which requires the adaptation algorithms to be light, efficient and accurate. Previous results focused mainly on efficiency. This paper proposes an novel online model adaptation framework for not only efficiency but also optimal online adaptation. At first, an online optimal incremental simplex tableau (IST)algorithm is proposed, which approaches the model adaption by linear programming and produces the optimized model update in each step when a personalized training data is collected.But keeping online optimal in each step is expensive and may cause over-fitting especially when the online data is noisy. A Fast-IST approach is therefore proposed, which measures the deviation between the training data and the current model. It schedules updating only when enough deviation is detected. The efficiency of each update is further enhanced by running IST only limited iterations, which bounds the computation complexity. Theoretical analysis and extensive evaluations show that Fast-IST saves computation cost greatly, while achieving speedy and accurate model adaptation.It provides better model adaptation speed and accuracy while using even lower computing cost than the state-of-the art.

Radio-map based method has been widely used for indoor location and navigation, but remaining key challenges are: 1) laborious efforts to calibrate a fine-grained radio-map, and 2) the locating result inaccuracy and not robust problems due to random signal strength (RSS) noises. An efficient way to overcome these problems is to collect RSS signatures along indoor paths and utilize sequence matching to enhance the location robustness. But, due to problems of indoor path combinational explosion, random RSS loss during movement, and moving speed disparity during online and offline phases, how to exploit sequence matching in radio-map remains difficult. This paper proposes WarpMap, an efficient sequence-type radio-map model and an accurate indoor location method by dynamic warping. Its distinct features include: 1) an undirected graph model (Trace-graph) for efficiently calibrating and storing sequence-type radio-map, which overcomes the path combinational explosion and RSS miss-of-detection problems; 2) an efficient sub-sequence dynamic time warping (SDTW) algorithm for accurate and efficient on-line locating. We show SDTW can tolerate random RSS disparities at discrete points and handle the moving speed differences in on-line and offline phases. The impacts of different warping distance functions, RSS preprocessing techniques were also investigated. Extensive experiments in office environments verified the efficiency and accuracy of WarpMap, which can calibrated within ten minutes by one person for 1100m2 area and provides overall nearly 20% accuracy improvements than the state-of-the-art of radio-map method.