With a plethora of wearable IoT devices available today, we can easily monitor human activities, many of which are unconscious or subconscious. Interestingly, some of these activities exhibit distinct patterns for each individual, which can provide an opportunity to extract useful features for user authentication. Among those activities, walking is one of the most rudimentary and mundane activity. Considering each individual's unique walking pattern, gait, which is the pattern of limb movements during locomotion, can be utilized as a biometric feature for user authentication. In this paper, we propose a lightweight seamless authentication framework based on gait (LiSA-G) that can authenticate and identify users on the widely available commercial smartwatches. Unlike the existing works, our proposed framework extracts not only the statistical features but also the human-action-related features from the collected sensor data in order to more accurately and efficiently reveal distinct patterns. Our experimental results show that our framework achieves a higher authentication accuracy (i.e., an average equal error rate (EER) of 8.2%) in comparison with the existing works while requiring fewer features and less amount of sensor data. This makes our framework more practical and rapidly deployable in the wearable IoT systems with limited computing power and energy capacity.
Crowdsourcing can be applied to the Internet-of-Things (IoT) systems to provide more scalable and efficient services to support various tasks. As the driving force of crowdsourcing is the interaction among participants, various incentive mechanisms have been proposed to attract and retain a sufficient number of participants to provide a sustainable crowdsourcing service. However, there exist some gaps between the modeled entities or markets in the existing works and those in reality: 1) dichotomous task valuation and workers' punctuality, and 2) crowdsourcing service market monopolized by a platform. To bridge those gaps of such impractical assumption, we model workers' heterogeneous punctuality behavior and task depreciation over time. Based on those models, we propose an Expected Social Welfare Maximizing (ESWM) mechanism that aims to maximize the expected social welfare by attracting and retaining more participants in the long-term, i.e., multiple rounds of crowdsourcing. In the evaluation, we modeled the continuous competition between the ESWM and one of the existing works in both short-term and long-term scenarios. Simulation results show that the ESWM mechanism achieves higher expected social welfare and platform utility than the benchmark by attracting and retaining more participants. Moreover, we prove that the ESWM mechanism achieves the desirable economic properties: individual rationality, budget-balance, computational efficiency, and truthfulness.
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