Traditionally, vehicle tracking is accomplished using pre-deployed video camera networks, which relies on stationary cameras and searches for the target vehicle from videos. In this work, we develop CrowdTracking, i.e., a crowd tracking system that people can collaboratively keep track of the moving vehicle by taking photos, especially in areas where video cameras are deficient. In other words, the underlying support of CrowdTracking is mobile crowdsensing. Several novel ideas underpin CrowdTracking. First, the vehicle can be rapidly localized by using both photographing contexts (including the location and the shooting direction) of the photographer and the road network. Second, the moving speed of the vehicle can be estimated according to two localization results and the trajectory will be predicted. As a result, through continuously collecting photos of the moving vehicle on different roads, the vehicle can be tracked and localized almost in real time. Through precisely localizing the specified vehicle, two optimization objectives are met: 1) maximizing the tracking coverage to the vehicle’s actual trajectory; 2) minimizing the number of participants who are assigned vehicle-tracking tasks. We evaluate the localization method with a real dataset and report about 6 meters error. We also evaluate the vehicle-tracking method of CrowdTracking using a synthetic data set, and experimental results validate its effectiveness and efficiency.

Mobile crowd photographing (MCP) is an emerging area of interest for researchers as the built-in cameras of mobile devices are becoming one of the commonly used visual logging approaches in our daily lives. In order to meet diverse MCP application requirements and constraints of sensing targets, a multifacet task model should be defined for a generic MCP data collection framework. Furthermore, MCP collects pictures in a distributed way in which a large number of contributors upload pictures whenever and wherever it is suitable. This inevitably leads to evolving picture streams. This paper investigates the multiconstraint-driven data selection problem in MCP picture aggregation and proposes a pyramid-tree (PTree) model which can efficiently select an optimal subset from the evolving picture streams based on varied coverage needs of MCP tasks. By utilizing the PTree model in a generic MCP data collection framework, which is called CrowdPic, we test and evaluate the effectiveness, efficiency, and flexibility of the proposed framework through crowdsourcing-based and simulation-based experiments. Both the theoretical analysis and simulation results indicate that the PTree-based framework can effectively select a subset with high utility coverage and low redundancy ratio from the streaming data. The overall framework is also proved flexible and applicable to a wide range of MCP task scenarios.

Pedestrians distracted by smartphones are easy to meet with various dangers when crossing or walking on the street, such as the obstacles on the sidewalk (e.g., temporary parking and road repairing). Existing works about pedestrian safety are mostly based on the sensing capabilities from a single device. The surrounding information that can be learned, however, is quite limited or incomplete. Therefore, in many cases the dangers cannot be detected and the pedestrians cannot be alerted. In this paper, a novel system called CrowdWatch is proposed, which leverages mobile crowd sensing and crowd intelligence aggregation to detect temporary obstacles and make effective alerts for distracted walkers. To detect obstacles, we first study the regular rules of pedestrians’ avoidance behaviors from the aspects of turn-making and visual contexts. The Dempster–Shafer evidence theory is then used to fuse the behavior and visual contexts, and further calculate the confidence of obstacle existence. Afterwards, we leverage the features of pedestrians’ traces to characterize an appropriate dangerous area, which is used to alert distracted walkers. The conducted experiments with 36 participants and different obstacle settings indicate that the crowd-intelligence-based obstacle detection method is effective and the accuracy of reminding attains 83.3%.

In order to achieve automatic behavioral monitoring of farming animals, this paper proposes a convolutional neural network-based behavioral recognition method for lactating sows. The behavioral data streams of lactating sows are collected by wearable sensors embedded with an 3D-accelerometer and a 3D-gyroscope and used to recognize six types of behaviors, including movement, drinking, eating, nursing, sleeping and lying. Among these behaviors, nursing, lying and sleeping can be classified as similar static behaviors. Based on the action images constructed with sensor data streams, convolutional neural networks is leveraged for the purpose of distinguishing static behaviors of the lactating sow. To address the problem of insufficient training data, we use the data augmentation technique. Experimental results verify the data augmentation method’s effectiveness and show that our proposed behavioral monitoring method has greater advantages in terms of accuracy than traditional machine learning methods. The research results have implications for behavioral monitoring and health assessment of lactating sows.

Traffic congestion reduces the working efficiency of taxis, thus lowering the income of taxi drivers. In most cases, taxi drivers will choose the least time-consuming route, which is usually the shortest in distance to the destination. However, during rush hours when traffic flow is heavy, the shortest route may not be the most time-efficient. A detour sometimes can save time for drivers but that may highly increase the fare for passengers. In order to optimize the balance between fare and profit, this paper proposes a framework, called ProfitMax, which will recommend a fixed price and a more profitable route by assigning the most suitable taxi for online taxi-hailing. The taxi's profit of completing an order is calculated by the income per minute. We use drivers' personal driving habit to estimate the basic cost of an order and then recommend the most profitable route. ProfitMax trains the personal fuel-consumption model and estimates the fuel consumption on drivers' smart devices. We use a real taxiing data set, including one-month taxis' GPS trajectories and car-OBD (on-board-diagnose) readings, for the performance evaluation. Experimental results show that ProfitMax estimated the fuel consumption more accurate than baselines and can also save more than 10% fuel.

Mobile crowdsensing is a special data collection manner which collects data by smart phones taken by people every day. It is essential to pick suitable workers for different outdoor tasks. Constrained by participants’ locations and their daily travel rules, they are likely to accomplish light outdoor tasks by their way without extra detours. Previous researchers found that people prefer to accomplish a certain number of tasks at a time; thus, we focus on assigning light outdoor tasks to workers by considering two optimization objectives, including (1) maximizing the ratio of light tasks for different workers and (2) maximizing the worker’s satisfaction on assigned tasks. This task allocation problem is a non-deterministic polynomial-time-hard due to two reasons, that is, tasks and workers are many-to-many relationships and workers move from different places to different places. Considering both optimization objectives, we design the global-optimizing task allocation algorithm, which greedily selects the most appropriate participant until either no participant can be chosen or no tasks can be assigned. For the purpose of emulating real scenarios, different scales of maps, tasks, and workers are simulated to evaluate algorithms. Experimental results verify that the proposed global-optimizing method outperforms baselines on both maximization objectives.