Just-in-Time Memoryless Trust for Crowdsourced IoT Services

We propose just-in-time memoryless trust for crowdsourced IoT services. We leverage the characteristics of the IoT service environment to evaluate their trustworthiness. A novel framework is devised to assess a service's trust without relying on previous knowledge, i.e., memoryless trust. The framework exploits service-session-related data to offer a trust value valid only during the current session, i.e., just-in-time trust. Several experiments are conducted to assess the efficiency of the proposed framework.Comment: 8 pages, Accepted and to appear in 2020 IEEE International Conference on Web Services (ICWS). Content may change prior to final publicatio

Spatio-temporal composition of crowdsourced services

We propose a new composition approach for crowdsourced services based on dynamic features such as spatio-temporal aspects. The proposed approach is defined based on a formal crowdsourced service model that abstracts the functionality of crowdsourced data on the cloud in terms of spatio-temporal features. We present a new QoS-aware spatio-temporal union composition algorithm to efficiently select the optimal crowdsourced composition plan. Experimental results validate the performance of the proposed algorithm