Aiming at the problems of low coverage rate and uneven distribution of nodes in Wireless Sensor Network （WSN）， a node coverage optimization strategy based on Multi-strategy Improved Butterfly Optimization Algorithm （MIBOA） was proposed. Firstly， the basic Butterfly Optimization Algorithm （BOA） was combined with Sparrow Search Algorithm （SSA） to improve the search process. Secondly， the adaptive weight coefficient was introduced to improve the optimization accuracy and convergence speed. Finally， the current best individual was perturbed by Cauchy mutation to improve the robustness of the algorithm. The optimization experiment results on benchmark functions show that， MIBOA can basically solve the optimal value of the test function within 3 seconds， and the average accuracy of convergence is improved by 97.96% compared with BOA. MIBOA was applied to the WSN node coverage optimization problem. Compared with optimization results of BOA and SSA， the node coverage rate was improved by 3.63 percentage points at least. Compared with the Improved Grey Wolf Optimization algorithm （IGWO）， the deployment time was shortened by 145.82 seconds. Compared with the Improved Whale Optimization Algorithm （IWOA）， the node coverage rate was increased by 0.20 percentage points and the time was shortened by 1 112.61 seconds. In conclusion， MIBOA can improve the node coverage rate and reduce the redundant coverage rate， and effectively prolong the lifetime of WSN.

Inferring work locations for users based on spatiotemporal data is important for real-world applications ranging from product recommendation, precise marketing, transportation scheduling to city planning. However, the problem of location inference based on urban surveillance data has not been explored. Therefore, a work location inference method was proposed for vehicle owners based on the data of traffic surveillance with sparse cameras. First, the urban traffic periphery data such as road networks and Point Of Interests (POIs) were collected, and the preprocessing method of road network matching was used to obtain a real road network with rich semantic information such as POIs and cameras. Second, the important parking areas, which mean the candidate work areas for the vehicles were obtained by clustering Origin-Destination (O-D) pairs extracted from vehicle trajectories. Third, using the constraint of the proposed in/out visiting time pattern, the most likely work area was selected from multiple area candidates. Finally, by using the obtained road network and the distribution of POIs in the road network, the vehicle's reachable POIs were extracted to further narrow the range of work location. The effectiveness of the proposed method was demonstrated by comprehensive experimental evaluations and case studies on a real-world traffic surveillance dataset of a provincial capital city.

Real-time urban traffic monitoring has become an important part of modern urban management, and traffic big data collected by video monitoring is wildly applied to urban management and traffic control. However, such huge citywide monitoring traffic big data is rarely used for urban traffic and urban computing research. The vehicle type mining and application analysis were implemented on the citywide monitoring traffic big data of a provincial capital city. Firstly, three types of vehicles with important influence on urban traffic:periodic private car, taxi and public commuter bus were defined. And the corresponding mining method for each type of vehicles was proposed. Experiments on 120 million vehicle records collected from 1704 video monitoring points in Jinan demonstrated the effectiveness of the proposed definitions and mining methods. Secondly, with four communities as examples, the residents' traffic modes and the relationships between the modes and the distribution of surrounding Points of Interest (POI) were mined and analyzed. Moreover, the potential applications of the urban traffic big data incorporated with POI in urban planning, demand forecasting and preference recommendation were explored.