As social networks become a major source of information, predicting the outcome of information diffusion has appeared intriguing to both researchers and practitioners. By organizing and categorizing the joint efforts of numerous studies on popularity prediction, this article presents a hierarchical taxonomy and helps to establish a systematic overview of popularity prediction methods for microblog. Specifically, we uncover three lines of thoughts: the feature-based approach, time-series modelling, and the collaborative filtering approach and analyse them, respectively. Furthermore, we also categorize prediction methods based on their underlying rationale: whether they attempt to model the motivation of users or monitor the early responses. Finally, we put these prediction methods to test by performing experiments on real-life data collected from popular social networks Twitter and Weibo. We compare the methods in terms of accuracy, efficiency, timeliness, robustness, and bias. As far as we are concerned, there is no precedented survey aimed at microblog popularity prediction at the time of submission. By establishing a taxonomy and evaluation for the first time, we hope to provide an in-depth review of state-of-the-art prediction methods and point out directions for further research. Our evaluations show that time-series modelling has the advantage of high accuracy and the ability to improve over time. The feature-based methods using only temporal features performs nearly as well as using all possible features, producing average results. This suggests that temporal features do have strong predictive power and that power is better exploited with time-series models. On the other hand, this implies that we know little about the future popularity of an item before it is posted, which may be the focus of further research.

Spatial crowdsensing is a special kind of crowdsourcing which allocates tasks to workers in some special places where workers can sense data for them. Due to the lack of priori information about the quality of workers and the ground truth, selecting the most suitable workers, which can guarantee the quality of the sensing tasks, remains a great challenge. In this paper, we propose a novel framework which can choose the most reliable workers among available workers under constraint budget. We model the quality of workers through two factors, bias and variance, which describe the continuous feature of sensing tasks. Our framework first allocate some calibration tasks to calibrate the bias and then iteratively estimate the workers' variance more and more accurately. To choose more reliable workers, we design a novel Multi-Armed Bandit(MAB) algorithm which based on Upper Confidence Bounds scheme and combined with a weighted data aggregation scheme to estimate a more accurate ground truth of a sensing task. Futhermore, a dynamic budget allocation scheme is designed to achieve global optimization. In simulation experiments, we compare our algorithm with several baselines with a real world data set and it shows the effectiveness in inferring the ground truth with limited budget.

Sweep coverage plays an important role in many applications like data gathering, sensing coverage, and devices control. In this paper, we deal with the sweep coverage problem with multiple mobile sensors to periodically cover n targets in the surveillance region. We propose three constant-factor approximations, namely, CycleSplit, HeteroCycleSplit, and PathSplit, to minimize the longest sweep period of mobile sensors under different scenarios, respectively. CycleSplit deals with the minperiod sweep coverage problem (MPSC), in which each mobile sensor works independently along a predetermined trajectory cycle. It has an approximation ratio of (5 - 2/(n - m + 1)), which improves the best known approximation ratio of 5. HeteroCycleSplit is a 5α-approximation. It computes the sensor routes for heterogeneous velocity min-period sweep coverage problem (HVMPSC), where each mobile sensor has a different velocity. PathSplit is a 2-approximation for connected path minperiod sweep coverage problem (CPMPSC). It solves a variant problem of sweep coverage where we need to cover all the given edges. Besides, we also propose an optimal algorithm DP-MPSC for min-period sweep coverage problem in 1-D case. Finally, we provide various numerical experiments and comparisons with several previous work to validate the efficiency of our design.

Energy consumption is a fundamental and critical issue in wireless sensor networks. Mobile sensors consume much more energy during the movement than that during the communication or sensing process. Thus how to schedule mobile sensors and minimize their moving distance, while keeping the coverage requirement has great significance to researchers. In this paper, we study the target coverage problem in mobile sensor networks where all the targets need to be covered by sensors continuously. Our goal is to minimize the moving distance of sensors to cover all targets in the surveillance region, which is in Euclidean space. Here initially all the sensors are located at $k$ base stations. Thus, we define this problem as $k$ -Sink Minimum Movement Target Coverage. To solve this problem, we propose a polynomial-time approximation scheme, named Energy Effective Movement Algorithm (EEMA). We prove that the approximation ratio of EEMA is $1+\varepsilon $ and the time complexity is $n^{O(1/\varepsilon ^{2})}$ . We also propose a distributed version of EEMA (D-EEMA) for large-scale networks where EEMA is not efficient enough in practice. Finally, we conduct experiments to validate the efficiency and effectiveness of EEMA and D-EEMA.