DQ-Map: Dynamic Decision Query Mapping for Provisioning Safety-as-a-Service in IoT

Abstract

In this work, we propose a dynamic decision query mapping mechanism, DQ-Map, for provisioning Safety-as-a-Service (Safe-aaS) (Roy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2018). A Safe-aaS infrastructure provides customized safety-related decisions simultaneously to multiple end-users. We consider road transportation as the application scenario of Safe-aaS and termed the safety-related decision to be delivered to the end-users as decision queries (DQs). These DQs are generated according to the decision parameters selected by the end-users. The primary aim of our proposed work is to reduce the total number of sensor nodes required to generate safety-related decisions, which minimizes both energy and time consumption. Further, the requested DQs are processed and a decision is generated in three different stages. First, the DQs are categorized as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">emergency decision query</i> (EDQ) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nonemergency decision query</i> (NEDQ), depending upon the type of vehicle from where the end-users have requested safety services. The EDQs and NEDQs are mapped with the stored decisions present in the database of the decision virtualization layer during the second level. In case of mismatch with the stored decisions in the database, EDQs are directly executed from the sensor nodes deployed at a particular geographical location or into the vehicles, in the device layer of the Safe-aaS infrastructure. In the third level, the similarity score of NEDQs, which do not match with the parameters of the stored decisions, is computed. Based on the number of similar decision parameters present in them, the similarity score is computed. Extensive simulation results of the proposed scheme, DQ-Map, depict that the amount of energy consumed and time required to generate a decision is reduced by 55.16% and 54.55%, respectively, compared to the traditional Safe-aaS architecture.