Data communication burden

This section provides an evaluation of the communication burden induced from the messages that the devices exchange. DARSIS is an opportunistic system that performs collaborative sensing to compute the relative orientation of the users and speed up the sensing process. The devices exchange the mutual Bluetooth RSSI measurements and the facing directions of their users.

Each message the devices exchange induces a certain communication burden. In par- ticular, the size of each message is 186 bytes. The number of messages sent by a device depends on the number of devices in the vicinity. When there are n devices in vicinity, a device will transmit at most n ∗ (message size + header) bytes of data during one infer- ence period. Thus, as both the header and the message size are constant, the message complexity of our system isO(n log n) for each interval of sensing and inference.

4.6

Conclusion

This chapter presented the design, analysis and evaluation of the DARSIS system that detects social interactions through smartphones in an opportunistic manner. Towards the realisation of the DARSIS system several contributions were achieved. First, an accurate and reliable interpersonal distance estimation technique for interaction zone and proximity detection that was introduced in Chapter 3 was incorporated. Sec- ondly, a relative orientation computation method was developed and incorporated in the DARSIS system that addresses the absence of facing direction in previous works and the dependence of on-body position. Thirdly, a collaborative sensing component was introduced to allow information exchange such as Bluetooth RSSI samples and users’ facing directions in order to speed up the sensing and inference process. Fourthly, the development of a generic analytical model was introduced to estimate the existence of social interaction based on interpersonal distance and relative orientation with a cer- tain probability. From this analytical model and the probability of occurrence, an error model was derived for the DARSIS system and further its expected error.

DARSIS system and the proposed analytical model were evaluated in three different indoor environments. The number of participants in the study was quite small (5 participants) and the standard deviation of the error is large relative to the separation of the mean error. In order to empower the evaluation results, as future work it is proposed to perform evaluation of the error of the DARSIS system and the proposed analytical model with a larger number of participants to minimise the standard deviation of the error relative to the mean error.

DARSIS is a privacy-preserving system as it performs the inference online and does not transmit the data to third-party components. It does not depend on any external hardware, therefore there are no mobility restriction for the operation of the system. Understanding social interactions and quantifying the related context provide valuable information for inferring different aspect of social behaviour. The DARSIS system is an initial step towards ubiquitous and pervasive observation of real-world social networks with applications in healthcare, the Internet of Things, epidemiology, marketing and others. One of the aspects of social behaviour that are related to social interactions

is the trust relationships among users. Trust is a fundamental notion based on which society has been build, from the rules and law of a society to the financial transactions among people. Understanding these trust relationships among user would improve the sustainability of future smart cities, create relationships among people without any prior knowledge and advance the trustworthiness of the social environment. An initial attempt towards understanding trust relationships among people is presented in the next chapter, were a real-world social graph is extracted by leveraging the social interactions detected by the DARSIS system. Furthermore, based on the social relations that the real-world social graph provides and combined with the contextual information from the daily social interaction, people trust relationships are inferred.

Quantifying Real-world Trust

Relationships through Social

Interactions

This chapter introduces a novel approach to quantify trust relationships among peo- ple based on detected social interaction through off-the-shelf mobile phones. Previous chapter presented and evaluated an opportunistic and collaborative sensing system to detect real-world social interactions based on smartphones. In this chapter, initially the information provided by the social interaction detection system are leveraged to extracted a novel real-world social graph that considers the social relation among peo- ple. Following the extraction of a real-world social graph, the derived social relation among people is combined with contextual information from the social interaction de- tection in order to quantify the trust relationships among people. A proof-of-concept evaluation was performed, where people were placed in an indoor environment and started to interact in a real-world situation, providing some initial insights regarding the applicability of the proposed approach.

5.1

Introduction

Trust is an important factor that plays a significant role in the structure of our society today. Fields such as psychology, organisational engineering, marketing and informat- ics have focused on understanding and measuring trust relationships. Psychological and emotional well-being have been correlated with trust, where a trusted person tend to be happier, more open to new relations and less neurotic in social situations [216]. Researchers focused on understanding and quantifying trust in various contexts. In- ternet applications [217], on-line social networks [218], on-line service provisioning sys- tems [219], Internet of Things [220] [221] and many others constitute a significant background for understanding trust.

Literature initially strived to measure trust relationships among people through less automated methods including questionnaires and surveys. These methods induce a considerable amount of error due to the involvement of the human factor [16]. Sev- eral techniques focused on understanding trust relationships among people in on-line social networks. These techniques consider information retrieved from users’ social accounts. Research has indicated that on-line and real-world social networks may be different, as in on-line social networks users tend to have a large amount of false positive relations [222]. Until now, there is no prior work that was able to quantify trust rela- tionships among people through smartphones based on contextual information derived from real-world social interactions and social relations.

The starting point of the research is whether smartphones are able to provide appro- priate contextual information extracted from daily interaction to create a real-world social graph and derive trust relationships among people. To initiate this research an assumption is taken, while people participate in a social interaction the level of trust and trustworthiness among them increases [223]

This chapter presents MobTrust, the first work towards quantifying trust relationships among people based on real-world interactions through smartphones. To support this work, a social interaction detection mechanism based on off-the-shelf smartphones is leveraged [224]. A real-world social graph is developed, leveraging the social interaction detection information. The edges of the real-world social graph are weighted with the

social relationship of people inferred from their interaction zone [193] and the confidence of estimation. A hybrid probabilistic model is developed that leverages contextual information from the social interaction detection and the real-world social graph. The model is able to infer in real-time about the social relations and the level of trust among people, with a corresponding confidence.

The rest of the chapter is structured as follows. Section 6.2 includes the prior works related to measuring trust. The developed methodology for extracting the real-world social graph and quantifying trust relationships is described in Section 6.3. The setup of the evaluation experiment is outlined in Section 5.4 and the results are discussed in Section 6.6. Potential applications of the trust relationship quantification mechanism are provided in Section 5.6, followed by the overall conclusions in Section 5.7.

5.2

Background

Important research has been conducted on the development of trust models for the iden- tification of trusted peers in various networks including EigenTrust [225], TrustMe [226], PeerTrust [227] and PowerTrust [228]. However, the research in this work considers the trust quantification in real-world social situations. Prior works for quantifying social trust in on-line social networks and real-world social networks. A more extensive anal- ysis of the prior works for trust computation is provided in [218].