80 Observation time(day) Fig.11.Social relationships inference results based on different length of (a)Social relationships inference. (b)Social relationships groundtruth. observation time. Fig.10.Social relationships comparison between inference results and the groundtruth. Each point in the graph represents a volunteer and different 5)Relationships and Demographics Refinement:We find types of lines between points represent the different relation- ships between two volunteers.Compared to the groundtruth. that the inferred relationships and demographics results can be mutually complementary.We then adopt several rules for the the overall detection rate of social relationships inference is relationship and demographics refinement.For example,the 91%,suggesting that our system can efficiently detect various relationships from surrounding AP information.In addition. family relationship between a male and a female is refined as the couple relationship or married;the collaborator between a our system also detects hidden relationships,which represent faculty and a student (or a company supervisor and a software the potential relationship that is recognizable by our system but unknown to the two volunteers due to the lack of face-to-face engineer)is refined as the advisor-student (or supervisor employee)relationship. interactions.We find that certain relationships(e.g.,colleagues and neighbors)may contain such hidden relationship. VII.PERFORMANCE EVALUATION Table I shows the detailed statistics of our social relation- A.Experiment Methodology ships inference results.We observe that we achieve 100% 1)Data Collection:Due to the limitation of the man power, detection rate for Relatives,Family and Neighbor,whereas we choose the representative occupations,working hours and achieve 83.3%.94.1%.89.5%and 87.5%detection rate for age groups for experiments to evaluate the feasibility of our Friends,Team members,Collaborators and Colleagues,re- approach.We recruit 21 volunteers (i.e.,6 females and 15 spectively,indicating that our method can accurately detect males)across three cities to collect surrounding APs informa- different relationships based on interaction features character- tion in their daily lives for over 6 months.The volunteers ized from surrounding APs.For the misclassified relationships, age from 20 to 40 and are mainly from six occupations, one team-member relation is classified as collaborators due including financial analyst,Ph.D.candidate,Master student, to irregular working time;two collaborators are classified undergraduate,assistant professor,and software engineer.We as colleagues in the same building due to low interaction ask the volunteers to install a tool developed for data collection frequency.The overall inference accuracy is 95.8%when on their own phones and run it in the background throughout we compare the detected relationships with the groundtruth. every day during the experiments.The users are asked to fill a We further detect 10 hidden relationships (i.e.,9 colleagues questionnaire to input the groundtruth.The IRB is approved. and I neighbor),while these relationships are not realized by 2)Hardware and Software:We include a variety of An- the volunteers but can be derived from their questionnaires, droid mobile devices in the real experiments including Sam- indicating our system can accurately detect most relationships sung,Huawei,LG and Xiaomi.We develop a tool on Android in daily life. platform to collect information of surrounding APs at a given Figure 11 shows the relationships inference results under frequency,i.e.,4 scans/min,which is the AP scanning fre- different length of observation time.We observe that most quency of many android systems [23].For each scan,our tool regular relationships(i.e.,family,neighbor,team member)can collects the simple information of surrounding APs,including be detected in the first day.As for other relationships,since BSSIDs,SSID,scanning time stamp and RSS. their interactions do not occur every day,we need to observe 3)Evaluation Metrics:We use the following two metrics to for more days to make a decision.The relationship inference evaluate the performance of our inference:Detection Rate.The results become stable after 5~7 days,indicating that our ratio of correctly identified results over the total numbers in system can detect most relationships in people's daily life groundtruth.Inference Accuracy.The ratio of correct inference based on their social interactions in one week. results over the total number of inference results TABLE I SOCIAL RELATIONSHIPS INFERENCE B.Evaluation of Social Relationships Inference Relationships Groundtruth Inference Correct Hidden We first examine the performance of social relationships Relatives 2 0 Fnends 0 inference from surrounding Wi-Fi APs.Figure 10 shows the Icam members 16 16 comparison between the inferred social relationships (i.e.,Fig- Collaborators 18 17 Colleagues 24 2 21 ure 10(a))among the 21 volunteers and the groundtruth from Family 6 6 6 the questionnaire(i.e.,Figure 10(b))in graphs of relationships. NeighborFamily Neighbor Team member Collaborator Colleagues in the same building Friend Person Family Neighbor Team member Collaborator Colleagues in the same building Friend Person (a) Social relationships inference. (b) Social relationships groundtruth. Fig. 10. Social relationships comparison between inference results and the groundtruth. 5) Relationships and Demographics Refinement: We find that the inferred relationships and demographics results can be mutually complementary. We then adopt several rules for the relationship and demographics refinement. For example, the family relationship between a male and a female is refined as the couple relationship or married; the collaborator between a faculty and a student (or a company supervisor and a software engineer) is refined as the advisor-student (or supervisor￾employee) relationship. VII. PERFORMANCE EVALUATION A. Experiment Methodology 1) Data Collection: Due to the limitation of the man power, we choose the representative occupations, working hours and age groups for experiments to evaluate the feasibility of our approach. We recruit 21 volunteers (i.e., 6 females and 15 males) across three cities to collect surrounding APs informa￾tion in their daily lives for over 6 months. The volunteers age from 20 to 40 and are mainly from six occupations, including financial analyst, Ph.D. candidate, Master student, undergraduate, assistant professor, and software engineer. We ask the volunteers to install a tool developed for data collection on their own phones and run it in the background throughout every day during the experiments. The users are asked to fill a questionnaire to input the groundtruth. The IRB is approved. 2) Hardware and Software: We include a variety of An￾droid mobile devices in the real experiments including Sam￾sung, Huawei, LG and Xiaomi. We develop a tool on Android platform to collect information of surrounding APs at a given frequency, i.e., 4 scans/min, which is the AP scanning fre￾quency of many android systems [23]. For each scan, our tool collects the simple information of surrounding APs, including BSSIDs, SSID, scanning time stamp and RSS. 3) Evaluation Metrics: We use the following two metrics to evaluate the performance of our inference: Detection Rate. The ratio of correctly identified results over the total numbers in groundtruth. Inference Accuracy. The ratio of correct inference results over the total number of inference results. B. Evaluation of Social Relationships Inference We first examine the performance of social relationships inference from surrounding Wi-Fi APs. Figure 10 shows the comparison between the inferred social relationships (i.e., Fig￾ure 10(a)) among the 21 volunteers and the groundtruth from the questionnaire (i.e., Figure 10(b)) in graphs of relationships. Observation time (day) 159 Number of relationships 0 20 40 60 80 Family Neighbor Team member Collaborators Colleagues Relatives Customers Friends Fig. 11. Social relationships inference results based on different length of observation time. Each point in the graph represents a volunteer and different types of lines between points represent the different relation￾ships between two volunteers. Compared to the groundtruth, the overall detection rate of social relationships inference is 91%, suggesting that our system can efficiently detect various relationships from surrounding AP information. In addition, our system also detects hidden relationships, which represent the potential relationship that is recognizable by our system but unknown to the two volunteers due to the lack of face-to-face interactions. We find that certain relationships (e.g., colleagues and neighbors) may contain such hidden relationship. Table I shows the detailed statistics of our social relation￾ships inference results. We observe that we achieve 100% detection rate for Relatives, Family and Neighbor, whereas achieve 83.3%, 94.1%, 89.5% and 87.5% detection rate for Friends, Team members, Collaborators and Colleagues, re￾spectively, indicating that our method can accurately detect different relationships based on interaction features character￾ized from surrounding APs. For the misclassified relationships, one team-member relation is classified as collaborators due to irregular working time; two collaborators are classified as colleagues in the same building due to low interaction frequency. The overall inference accuracy is 95.8% when we compare the detected relationships with the groundtruth. We further detect 10 hidden relationships (i.e., 9 colleagues and 1 neighbor), while these relationships are not realized by the volunteers but can be derived from their questionnaires, indicating our system can accurately detect most relationships in daily life. Figure 11 shows the relationships inference results under different length of observation time. We observe that most regular relationships (i.e., family, neighbor, team member) can be detected in the first day. As for other relationships, since their interactions do not occur every day, we need to observe for more days to make a decision. The relationship inference results become stable after 5 ∼ 7 days, indicating that our system can detect most relationships in people’s daily life based on their social interactions in one week. TABLE I SOCIAL RELATIONSHIPS INFERENCE. Relationships Groundtruth Inference Correct Hidden Relatives 2 2 2 0 Friends 6 5 5 0 Team members 17 16 16 0 Collaborators 19 18 17 0 Colleagues 24 23 21 9 Family 6 6 6 0 Neighbor 1 1 1 1