Selective versus Non-Selective Acquisition of Crowd-Solicited IoT Data and Its Dependability

With the widespread availability of built-in and nondedicated sensors, acquiring crowd-solicited Internet of Things (IoT) data has become possible and convenient via smart mobile devices. Ensuring the dependability of crowd- solicited IoT data is a grand challenge since potential threats originated by adversaries are inevitable. When IoT data is acquired via nondedicated sensors in an opportunistic or participatory manner, the users/sensing data providers are not given the choice to select sensing tasks based on the other participants in the vicinity. In this paper, we perform a thorough feasibility analysis of two possible data acquisition approaches for crowd-solicited IoT data: 1) A mobile edge-based approach, namely the Selective and Reputation-aware Recruitment (SRR) which enables sensing service providers to join a highly dependable community, 2) Cloud- based non-selective reputation-aware recruitment (NSR). The community selectiveness of sensing data providers is basically turning off the corresponding sensors in the mobile devices while having the rest of the sensor array remain on. We present a framework where the selectiveness can be handled at the IoT gateways as data service functions to bypass the communication latency with the cloud-based crowd- sensing platform, and taking advantage of edge- based computing power. Our feasibility study through simulations shows that dependable community selectiveness does not lead to significant energy savings while slight improvement of user and platform utilities indicate the possible viability of cloud-based non-selective data acquisition of crowd- solicited IoT data.