Wi-Fi AP list tim Dynamic Searching window Wi-Fi Access Point Time-series Ⅲ-II AP lists to be se Staying Segment AP List-hased Staying/Traveling d AP for all scan Detection and Grouping d AP fer all ance Distritrution-based Physical Closeness-based Esegment [T之h anted AP Int ms 1+ Daily Place and Daily Routine-based Stavine Activity Inference Segment Group Categorization Activity Feature Extraction and Fin Fig.3.Staying/traveling segmentation leveraging dynamic searching windows e-ra Home to analyze the overlapped AP lists over consecutive scans. Characterization and Closeness-based Social Relationships Classification to infer when,where and how closely people in- teract with each other for inferring their possible relationships avior-based I such as family,neighbors,colleagues,and friends.To derive a hips Infcrence user's demographics.Behavior-based Demographics Inference applies Daily Activity-based Behavior Derivation to abstract people's various behaviors including working behaviors,home Family Neighbors Colleagues Friends on Gender Religion Mariage behaviors and leisure behaviors,based on the activities at Social Relationships Demographics daily places.It then utilizes Behavior-based Decision Rule to Fig.2.Wi-Fi AP distribution-based social relationships and demographics infer users'demographic information(e.g.,occupation,gender, inference framework. marriage and religion)based on the behavior abstraction. addresses and RSS,to infer fine-grained social relationships At last,the Associate Reasoning can be applied to social and demographics.Figure 2 presents our system flow. relationships and demographics to improve the accuracy of First,the Staving Segment Detection and Grouping com- inference results,such as identifying the specific role of the ponent detects and characterizes users'daily visited places user in a relationship (e.g.,husband-wife and advisor-student). in three steps.AP List-based Staying/Traveling Segmentation analyzes the overlap of the AP lists over consecutive scans IV.STAYING SEGMENT GROUP DETECTION AND and divides the time-series into staying and traveling periods CHARACTERIZATION Staying Segment Characterization estimates the significance A.AP List-based Staying/Traveling Segmentation of each surrounding AP by calculating its appearance rate As observed in the preliminary study of Figure 1(b),the within the staying segment.It then categorizes the APs by discovered AP BSSID lists of consecutive scans have large their significance to describe the spatial information of each overlaps when the user stays at the same place,while the staying segment.The spatially close-by staying segments are similarity of the AP lists is rapidly diminished when the user then grouped together as one unique place by using Closeness- moves to a different place.We thus take the advantage of the based Staying Segment Grouping. AP list similarity (i.e.BSSID list similarity)in consecutive The next component is to derive the activities at daily places scans to detect the staying and traveling segments.We define which is an important building block of social relationships staying segment as the Wi-Fi AP-list time-series segment that and demographics inference.It is carried out by using Daily captures the temporal and spatial information when the user Place and Activity Inference,which involves Daily Routine- stays at a location.And we analyze the overlap of the AP lists based Staying Segment Group Categorization and Daily Ac-within a dynamic searching window of consecutive scans to tivity Feature Extraction and Fine-grained Place Context In- perform staying segmentation ference.Daily Routine-based Staying Segment Categorization In particular,Figure 3 illustrates the proposed AP List- classifies the grouped staying segments (i.e.unique places) based Staying/Traveling Segmentation in identifying the stay- into three contextual categories (i.e.home,leisure and work- ing segment n.The dynamic searching window starts at t place)based on people's daily routines.At last,Daily Activity and iteratively expands to the next scan.In each iteration, Feature Extraction and Fine-grained Place Context Inference we analyze the overlapped APs of all the scans within the derives people's activity features including the staying time searching window.The number of solid dots at each scanning slots,duration and activeness and assigns detailed contextual time ti(i=1.2....)indicates the number of overlapped APs information to these places by leveraging the derived activity that are found within the window from t to t.When the features and geo-information,such as restaurants or stores in searching window iteratively expands to the next scan,the leisure places,campus or office buildings in workplaces. number of overlapped APs may decrease.When no overlapped Finally,our system infers users'social relationships and AP is found in the expanded searching window (e.g.,the demographics based on the derived activities at daily places.window from f tot),such searching window is identified as In particular,it first calculates the physical closenesses of the one possible staying segment.We note that because it may take interactions between users.It then uses Interaction Segment several scans to travel out of an AP's range,this approach can                     ! "                 !   #   !  "       "    #"      "        $   %&         #" $' "$ $  * +  ;  ! ;  ;   * +  $' ' "*+  '  +                    !                                    /'&      #"                  Fig. 2. Wi-Fi AP distribution-based social relationships and demographics inference framework. addresses and RSS, to infer fine-grained social relationships and demographics. Figure 2 presents our system flow. First, the Staying Segment Detection and Grouping com￾ponent detects and characterizes users’ daily visited places in three steps. AP List-based Staying/Traveling Segmentation analyzes the overlap of the AP lists over consecutive scans and divides the time-series into staying and traveling periods. Staying Segment Characterization estimates the significance of each surrounding AP by calculating its appearance rate within the staying segment. It then categorizes the APs by their significance to describe the spatial information of each staying segment. The spatially close-by staying segments are then grouped together as one unique place by using Closeness￾based Staying Segment Grouping. The next component is to derive the activities at daily places which is an important building block of social relationships and demographics inference. It is carried out by using Daily Place and Activity Inference, which involves Daily Routine￾based Staying Segment Group Categorization and Daily Ac￾tivity Feature Extraction and Fine-grained Place Context In￾ference. Daily Routine-based Staying Segment Categorization classifies the grouped staying segments (i.e. unique places) into three contextual categories (i.e. home, leisure and work￾place) based on people’s daily routines. At last, Daily Activity Feature Extraction and Fine-grained Place Context Inference derives people’s activity features including the staying time slots, duration and activeness and assigns detailed contextual information to these places by leveraging the derived activity features and geo-information, such as restaurants or stores in leisure places, campus or office buildings in workplaces. Finally, our system infers users’ social relationships and demographics based on the derived activities at daily places. In particular, it first calculates the physical closenesses of the interactions between users. It then uses Interaction Segment      ାଵ௠ݐ ௠ݐ ଵ௠ିݐ ଶ௠ିݐ ଷݐ ଶݐ ଵ   ݐ                ! "" #    $% $$ & "" #    $% $$ ଷ௠ିݐ      ݉െͳ ܶ௦ ൌ ݐ ௠െ ݐଵ  ݄ݐ ൐ ௦ܶ ݉  ݄ݐ ൏ ௦ܶ    % $$        #   '          '  #  Fig. 3. Staying/traveling segmentation leveraging dynamic searching windows to analyze the overlapped AP lists over consecutive scans. Characterization and Closeness-based Social Relationships Classification to infer when, where and how closely people in￾teract with each other for inferring their possible relationships such as family, neighbors, colleagues, and friends. To derive a user’s demographics, Behavior-based Demographics Inference applies Daily Activity-based Behavior Derivation to abstract people’s various behaviors including working behaviors, home behaviors and leisure behaviors, based on the activities at daily places. It then utilizes Behavior-based Decision Rule to infer users’ demographic information (e.g., occupation, gender, marriage and religion) based on the behavior abstraction. At last, the Associate Reasoning can be applied to social relationships and demographics to improve the accuracy of inference results, such as identifying the specific role of the user in a relationship (e.g., husband-wife and advisor-student). IV. STAYING SEGMENT GROUP DETECTION AND CHARACTERIZATION A. AP List-based Staying/Traveling Segmentation As observed in the preliminary study of Figure 1(b), the discovered AP BSSID lists of consecutive scans have large overlaps when the user stays at the same place, while the similarity of the AP lists is rapidly diminished when the user moves to a different place. We thus take the advantage of the AP list similarity (i.e. BSSID list similarity) in consecutive scans to detect the staying and traveling segments. We define staying segment as the Wi-Fi AP-list time-series segment that captures the temporal and spatial information when the user stays at a location. And we analyze the overlap of the AP lists within a dynamic searching window of consecutive scans to perform staying segmentation. In particular, Figure 3 illustrates the proposed AP List￾based Staying/Traveling Segmentation in identifying the stay￾ing segment n. The dynamic searching window starts at t1 and iteratively expands to the next scan. In each iteration, we analyze the overlapped APs of all the scans within the searching window. The number of solid dots at each scanning time ti(i = 1,2,...) indicates the number of overlapped APs that are found within the window from t1 to ti. When the searching window iteratively expands to the next scan, the number of overlapped APs may decrease. When no overlapped AP is found in the expanded searching window (e.g., the window from t1 to tm), such searching window is identified as one possible staying segment. We note that because it may take several scans to travel out of an AP’s range, this approach can