QoS-Aware Data Management Mechanisms for Optimal Resource Utilisation in Crowd-Assisted Shared Sensor Networks

In this study, we focus on the problem of managing a hybrid, shared IoT-based monitoring system, in which stationary sensor devices are complemented with user-carried personal devices embedded with sensing capabilities. The envisioned crowd-assisted monitoring system must support the sharing of the sensing infrastructure among multiple concurrent sensing tasks that can have highly varying QoS requirements. In such a scenario, a key issue is to maximise the utilisation efficiency of the physical sensing resources and the QoS satisfaction of sensing tasks while limiting the redundancy of collected data. As in previous research, we advocate the use of an IoT Broker, an intermediary entity that (i) interacts with the IoT applications to collect their QoS requirements (i.e., spatial coverage, data notification frequency); and (ii) coordinates with the redundant sensor deployments and mobile devices to selectively activate and configure the data streams that are needed to fulfil application requirements in a cost-efficient way. Then, we have developed an optimisation framework to jointly select the set of physical sensing resources to activate and the data update frequency for maximising the overall sensing performance while limiting redundant data. A key feature of our proposed framework is to be privacy-friendly as it only requires coarse-grained space-time knowledge of device location. Extensive simulations under realistic WSN deployments and real-life mobility patterns confirm the efficiency of the proposed solution in terms of data-coverage gain and reduction of data redundancy with respect to classical non-hybrid monitoring systems.