On Minimizing Sensing Time via Data Sharing in Collaborative Internet of Things

In order to improve the utilization efficiency of Internet of Things (IoT), more collaboration is preferable in the collection and exchange of data. When multiple applications/tasks request data from a sensor, we can make use of data sharing among the tasks as long as the data meets the time-sensitive QoS requirements of the tasks. This would in turn reduce both the total sensing time and the energy consumption, which is a significant concern in IoT systems. A practical question is how to design an efficient schedule to enable more data sharing and less sensing time. This paper addresses such a problem by developing algorithms with performance guarantees, respectively, for the offline and online scenarios. Two task models are studied, respectively, first-infirst-out (FIFO) task model and arbitrary deadline (AD) task model. We first provide an optimal algorithm for FIFO tasks in the offline case, and then we study the online case, in which data requests arrive dynamically without prior information. For FIFO tasks, we develop an online twocompetitive algorithm that always incurs a total sensing time no more than two times of the optimal solution. For AD tasks, we devise an online algorithm that is O(logL) competitive, where L is the maximum length of the time duration of the tasks. Simulation results validate the efficiency of our online algorithms.