More recently.Wi-Fi traffic monitoring and smartphone 3000 Surrounding Wi-Fi APs Apps have been used to infer users'demographic information. (Time-series of MAC 250 For example,Cheng et al.examine the user's Internet browsing addresses and R55] g2000 activities (e.g.,domain name querying,web browsing)by People's Activities a collecting their Wi-Fi traffic in public hotspots [4].They Daily Places 000 are able to reveal the travelers'identities,locations or social privacy.Huaxin et al.design an approach to infer user demo- Social nographics Relationships Information graphic information by sniffing the Wi-Fi traffic meta-data [5]. (a)Connection from surrounding APs to (b)lllustration of observed APs by Seneviratne et al.design a system to predict various user traits social relationships demographics. a user's smartphone in one day. by analyzing the snapshot of installed Apps [19].Different Fig.1.Preliminary studies. from the above work,we study the capability of examining the simple signal information of surrounding APs to derive leisure time)can be derived to reflect individual demographics demographic information without sniffing any Wi-Fi traffic or Furthermore,we observe that the same place or the places examining the installed Apps. in the neighborhoods may share some APs (e.g.,office and III.SYSTEM DESIGN restaurant 1).Their physical closeness may be obtained by A.Preliminaries checking how many surrounding APs they share,which is Environment-Behavior research reveals that an individual's useful for analyzing social interactions. activities such as work-related,household and leisure activities B.Challenges are related to the places they visit [21].And such activities Robust Daily Places and Activity Detection Using APs. at daily visited places can be analyzed and mined to infer Lacking the pre-knowledge of AP deployment,the accurate users'personal information such as social relationships and and robust detection of daily places and activities from ubiq- demographics [22].Thus by leveraging the users'activities at uitous APs is challenging.And the ubiquitous unstable and daily places as a bridge,we could start from the non-contextual mobile APs even add to the difficulties.Additionally,the daily surrounding AP information to infer users'social relationships places need to be abstracted with sufficient spatial resolution and demographics.This connection is depicted in Figure 1(a). (e.g.,differentiating rooms and floors)for further deriving The surrounding Wi-Fi APs reflect users'surrounding wireless users'mobility and their physical closeness during interaction. environments,which can be utilized to determine users'daily Determining the Context of Daily Places.Deriving the visited places and activities.The daily places in our work refer context of a user's daily visited places from the non-contextual to the abstract locations that users visit in their daily lives, AP signal information is challenging.Moreover,a place may such as home,workplace,restaurants,stores and churches.By exhibit different contexts to different users.For example,stores analyzing users'activities at daily places,we could derive the are leisure places to most people but the workplace to the social interactions between users and abstract individual's be- store staff.This requires us to search for the deep implication havior.Such information is then further utilized to mine users' behind the individual's activities at the place instead of relying social relationships and demographics.Note that contrary to on traditional place context based on the place function. the existing work in social relationships and demographics inference,we only utilize the availability of surrounding APs' Fine-grained Social Relationships Inference.Fine-grained relationships inference needs the information on not only simple signal information without requiring to sniff any Wi-Fi who have interactions but also on how closely they interact. traffic contents. Our systems needs to have the capability to define multiple To study how the surrounding APs can be utilized to detect a user's daily places and activities,we conduct preliminary closenesses between users.Furthermore,specifying the role of each user in a relationship (e.g.,husband or wife)may needs experiments by recording the APs on the user's smartphone at the regular rate of one scan per 15 seconds,because a Wi-Fi the assistance from demographic information (e.g.,gender). device usually scans every 5-15 seconds for providing the Demography Inference without Context.Inferring a user's user non-interrupted Wi-Fi connection to cope with the user's demographics with non-contextual simple signal information of surrounding APs is challenging.Different from the previous place change [23],[24].Figure 1(b)shows the recorded time- work relying on the content obtained from monitoring the Wi- series of a user's surrounding APs (differentiated by BSSIDs) Fi traffic,our system explores the possibility to abstract users' for one day,as well as the groundtruth of visited places.As behaviors based on their various activities at daily places for the AP index is assigned to each unique AP in sequence,the later observed AP has larger index.The observation is that demographic inference. the detected AP lists have large overlaps when the user stays C.System Overview at the same place,while the AP lists are distinct when the The basic idea of our system is to analyze users'activities user moves to a different daily place.This suggests that we at daily routine-based places that are derived from users' may utilize the changes of the observed AP list to detect the surrounding APs for fine-grained social relationships and user's daily visited places as well as the entrance/departure demographics inference.The proposed system takes as inputs time and the staying duration.Moreover,the user's activities the information of users'surrounding APs perceived by their at daily places (e.g.,the user's mobility at work and during smartphones at each scan,including the list of AP MACMore recently, Wi-Fi traffic monitoring and smartphone Apps have been used to infer users’ demographic information. For example, Cheng et al. examine the user’s Internet browsing activities (e.g., domain name querying, web browsing) by collecting their Wi-Fi traffic in public hotspots [4]. They are able to reveal the travelers’ identities, locations or social privacy. Huaxin et al. design an approach to infer user demo￾graphic information by sniffing the Wi-Fi traffic meta-data [5]. Seneviratne et al. design a system to predict various user traits by analyzing the snapshot of installed Apps [19]. Different from the above work, we study the capability of examining the simple signal information of surrounding APs to derive demographic information without sniffing any Wi-Fi traffic or examining the installed Apps. III. SYSTEM DESIGN A. Preliminaries Environment-Behavior research reveals that an individual’s activities such as work-related, household and leisure activities are related to the places they visit [21]. And such activities at daily visited places can be analyzed and mined to infer users’ personal information such as social relationships and demographics [22]. Thus by leveraging the users’ activities at daily places as a bridge, we could start from the non-contextual surrounding AP information to infer users’ social relationships and demographics. This connection is depicted in Figure 1(a). The surrounding Wi-Fi APs reflect users’ surrounding wireless environments, which can be utilized to determine users’ daily visited places and activities. The daily places in our work refer to the abstract locations that users visit in their daily lives, such as home, workplace, restaurants, stores and churches. By analyzing users’ activities at daily places, we could derive the social interactions between users and abstract individual’s be￾havior. Such information is then further utilized to mine users’ social relationships and demographics. Note that contrary to the existing work in social relationships and demographics inference, we only utilize the availability of surrounding APs’ simple signal information without requiring to sniff any Wi-Fi traffic contents. To study how the surrounding APs can be utilized to detect a user’s daily places and activities, we conduct preliminary experiments by recording the APs on the user’s smartphone at the regular rate of one scan per 15 seconds, because a Wi-Fi device usually scans every 5 - 15 seconds for providing the user non-interrupted Wi-Fi connection to cope with the user’s place change [23], [24]. Figure 1(b) shows the recorded time￾series of a user’s surrounding APs (differentiated by BSSIDs) for one day, as well as the groundtruth of visited places. As the AP index is assigned to each unique AP in sequence, the later observed AP has larger index. The observation is that the detected AP lists have large overlaps when the user stays at the same place, while the AP lists are distinct when the user moves to a different daily place. This suggests that we may utilize the changes of the observed AP list to detect the user’s daily visited places as well as the entrance/departure time and the staying duration. Moreover, the user’s activities at daily places (e.g., the user’s mobility at work and during                        ! ! " # $ $ (a) Connection from surrounding APs to (b) Illustration of observed APs by social relationships & demographics. a user’s smartphone in one day. Fig. 1. Preliminary studies. leisure time) can be derived to reflect individual demographics. Furthermore, we observe that the same place or the places in the neighborhoods may share some APs (e.g., office and restaurant 1). Their physical closeness may be obtained by checking how many surrounding APs they share, which is useful for analyzing social interactions. B. Challenges Robust Daily Places and Activity Detection Using APs. Lacking the pre-knowledge of AP deployment, the accurate and robust detection of daily places and activities from ubiq￾uitous APs is challenging. And the ubiquitous unstable and mobile APs even add to the difficulties. Additionally, the daily places need to be abstracted with sufficient spatial resolution (e.g., differentiating rooms and floors) for further deriving users’ mobility and their physical closeness during interaction. Determining the Context of Daily Places. Deriving the context of a user’s daily visited places from the non-contextual AP signal information is challenging. Moreover, a place may exhibit different contexts to different users. For example, stores are leisure places to most people but the workplace to the store staff. This requires us to search for the deep implication behind the individual’s activities at the place instead of relying on traditional place context based on the place function. Fine-grained Social Relationships Inference. Fine-grained relationships inference needs the information on not only who have interactions but also on how closely they interact. Our systems needs to have the capability to define multiple closenesses between users. Furthermore, specifying the role of each user in a relationship (e.g., husband or wife) may needs the assistance from demographic information (e.g., gender). Demography Inference without Context. Inferring a user’s demographics with non-contextual simple signal information of surrounding APs is challenging. Different from the previous work relying on the content obtained from monitoring the Wi￾Fi traffic, our system explores the possibility to abstract users’ behaviors based on their various activities at daily places for demographic inference. C. System Overview The basic idea of our system is to analyze users’ activities at daily routine-based places that are derived from users’ surrounding APs for fine-grained social relationships and demographics inference. The proposed system takes as inputs the information of users’ surrounding APs perceived by their smartphones at each scan, including the list of AP MAC