All walks of life and daily life are affected by human activities.Human activity recognition (HAR) has a wide range of application,and has been widely concerned.With the gradual development of smart phones,sensors are embedded in the phone to make the phone more intelligent and realize more flexible man-machine interaction.Modern people usually carry smart phones with them,so there is a wealth of information about human activities in the signals of mobile phone sensors.By extracting signals from the phone’s sensors,it is possible to identify users’ activities.Compared with other methods on the strength of computer vision,HAR on account of mobile phone sensors can better reflect the essence of human movements,and has the characteristics of low cost,flexibility and strong portability.In this paper,the current situation of HAR based on mobile phone sensors is described in details,and the system structure and basic principles of the main technologies are described and summarized in details.Finally,the existing problems and future development direction of HAR based on mobile phone sensors are analyzed.

In recent years,with the rapid development of technology,smart mobile devices have become a part of people’s lives.The popularity of smart mobile devices provides sufficient physical support for the realization of sonar perception theory.When the sonar signal is propagated,it is modulated by the propagation space and life activities,so it carries a wealth of life state and space information.The popularity of smartphones,the maturity of communication technology,and the innovative use of acoustic signals have enabled sonar sensing devices to achieve low-cost,fine-grained sensing collection and calculation.Utilizing acoustic signals in sonar sensing technology does not require the support of special hardware.With the unique concealment and its typical feature of high accuracy,the acoustic signals can calculate the surrounding space information.This article elaborates on the research history of acoustic signals in the field of spatial positioning and sensing technology,summarizes the basic principles of the main technologies,and finally analyzes the problems and future development trends of acoustic signals in mobile sensing application technology.

Crowdsourcing is a distributed problem solving mechanism using group intelligence.It is widely used in Internet application scenarios based on artificial intelligence activities,using large groups of users on the Internet to work together to solve complex problems that cannot be solved by one person.Taking the development and maintenance process of open source software as an example,participants jointly complete key tasks such as code writing and bug repair through specific platforms.Different from traditional business process management (BPM),collaborative processes in the crowdsourcing scenario face challenges such as undetermined process structure,and unpredictable timing and results,which bring great difficulties to the efficiency and quality control of crowdsourcing collaboration.In this paper,aiming at a series of collaborative behaviors produced by multiple participants according to the time sequence (embodied as text in the form of natural language),natural language processing and artificial intelligence are used to propose a restoration algorithm for the process of crowdsourcing collaboration.An empirical study is carried out on the case of personnel cooperation in the process of bug repair in the field of open source software development.The collaborative process of recovery is visualized,and the accuracy of process recovery algorithm is quantitatively compared.This research can help coordinators of crowdsourcing process (such as open source project managers) to understand the problem solving process more intuitively,and find the typical patterns of collaboration,so as to make an accurate prediction for the nature of the collaborative process of the new crowdsourcing task.

Crowdsourcing is an increasingly important area of computer applications,because it can address problems that difficult for computer to handle alone.For the openness of crowdsourcing,quality control becomes one of the important challenges.In order to ensure the effectiveness of truth inference,current researches leverage answers of trustful workers to infer truths by evalua-ting worker quality generally.However,most existing methods ignore the long-tail phenomena in crowdsourcing,and there is a lack of researches on the truth inference when the number of tasks completed by workers is generally small.Considering the characteristics of different task types,long-tail phenomenon and worker answers,this paper constructs the confidence interval of small samples to solve truth inference when the number of tasks completed by workers are generally small.Firstly,worker quality is pre-estimated according to the gold standard answer strategy,and different truth initialization methods are adopted according to the result of pre-estimated.Then,the confidence interval of small samples is constructed to evaluate worker quality accurately.Finally,task truths are inferred and worker quality is updated iteratively.In order to verify the effectiveness of the proposed me-thod,5 real datasets are selected to conduct experiments.Compared with the existing methods,the proposed method can solve the problem of the long tail phenomenon effectively,especially the number of tasks completed by each worker is generally small.The average accuracy of the proposed method for the single-choice tasks is as high as 93%,and higher than 16% of the bestperfor-mance of the existing methods.Meanwhile,the values of MAE and RMSE of the proposed method for the numerical tasks are lower than that of the existing methods.

With the development of mobile crowdsourcing,more and more tasks are published on crowdsourcing platforms.However,crowd workers choose tasks suitable for them will take a lot of time according to their interests,because there are a large number of tasks in the mobile crowdsourcing system.In addition,it is difficult for them to select the tasks that are most suitable for their own execution,because the crowd workers have no knowledge of the information of all tasks existing in the crowdsour-cing system.The tasks in the mobile crowdsourcing system have the spatio-temporal characteristic,which requires crowd workers to move to the specified region to complete the task within the specified time interval.However,crowd workers have their own works and life,in order to adapt to their daily movement,a mobile prediction model is proposed to predict the movement behavior of them.Based on the prediction results and the needs of crowd workers,a task recommendation model based on the movement trajectory of crowd workers is proposed to recommend tasks for crowd workers.Finally,a lot of simulations are carried out on two real data sets.The results prove that the proposed model has high accuracy and good adaptability.

Mobile crowdsensing system should offer the personalized privacy protection of users’ location to attract more users to participate in the task.However,due to the existence of malicious attackers,users’ enhanced privacy protection will lead to poor location availability and reduce the efficiency of task allocation.To solve this problem,this paper proposes a win-win game based on reinforcement learning.Firstly,two virtual entities of the trusted third party are used to simulate the interaction between users and the platform,one simulating user chooses the privacy budget to add noise to their locations and the other simulates the platform allocating tasks with users’ disturbed locations.Then,the interaction process is constructed as a game,in which the two virtual entities of interaction are the adversaries,and the equilibrium point is derived.Finally,the reinforcement learning method is used to try different location disturbance strategies and output an optimal location disturbance scheme.The experimental results show that the mechanism can optimize the task distribution utility while improving the user’s overall utility as much as possible,so that the user and the platform can achieve a win-win situation.

With the development of wireless communication technology,wireless sensing technology has been widely studied.This paper proposes vital signs monitoring method based on CSI phase.The method employs commodity WiFi to obtain CSI phase information.The liner transformation is used to reduce phase shift and delay interference caused by un-synchronization of transmitter and receiver.Hampel filter is implemented to filter out DC component and high frequency noises influenced by signal fading and multipath effects.Discrete wavelet transform is utilized to realize vital signs extraction.According to the characterizes of breathing and heartbeat frequency,multi-subcarrier fusion and Fast Fourier Transform algorithms are respectively employed to estimate breathing and heart rates.Experimental results show that the method can effectively capture vital signs in multiple scenarios.

Clustering is an effective method to improve the perception quality of Vehicular Crowd Sensing (VCS) and reduce costs.However,how to maximize the cluster stability while accounting for the high mobility of vehicles remains a challenging problem.Based on the communication characteristics of VANET,a clustering algorithm based on Fog Computing and Self-Assessment (FCSAC) is proposed,which divides VANET into many clusters,and each cluster selects a Master Cluster Head(MCH) for data dissemination.The results of vehicle cooperative perception in the cluster are given to the fog nodes by MCH,the vehicle mobility rate (VMR) is introduced to improve Master Cluster Head(MCH) election method,this parameter is calculated based on mobility metrics to satisfy the need for VANET great mobility.Then,this paper evaluates the impact of vehicle joining on cluster stability by defining scaling functions and weighting mechanisms.FCSAC strengthens clusters’ stability through the election of a Slave Cluster Head (SCH) in addition to the MCH.In order to improve the accuracy,timeliness,and effectiveness of traffic information,on the basis of fog computing,via chain collaboration traffic perception between the MCH,an accurate and comprehensive view of the local traffic perception is formed.Finally,the Veins simulation platform is used to eva-luate the performance.The results show that,compared with the CBRSDN algorithm and SACBR algorithm,the proposed algorithm performs better in terms of cluster stability,and effectively improves the throughput of VANET.Compared with the Fuzzy C-Means (FCM) algorithm,it has better traffic diversion capability and reduces the consumption of network communication.

It is a new idea of non-intrusive perception technology to track and analyze the dynamic change of group structure in WiFi environment by using smart phone.Based on the relationship between WiFi information difference and between-user distance,a method of WiFi dissimilarity computation is designed.According to the WiFi dissimilarity between users,the dissimilarity distance is statistically calculated,and then the GSGA-RSS algorithm is used to iteratively calculate the node coordinates.Finally,the hierarchical group structure is analyzed by DBSCAN.A method of LMD (location mean deviation) computation based on mass center is proposed,and experiments on groups structures of queues and ring topology under different between-user distances are conducted.The results show that the proposed approach can identify 85% of the groups with 94% precision for the cases with the minimum intergroup distance of 5 m and the maximum intragroup distance of 3 m.The LMD is about 0.5 for the queues with between-user distance of 0.5 m,and about 1 for the ring structure with between-user distance of 1 m.