Sharing-aware Data Acquisition Scheduling for Multiple Rules in the IoT

In the Internet-of-Things (IoT) environments, users define event-condition-action (ECA) rules, and expect IoT frameworks to evaluate conditions and take appropriate actions within a certain time limit after an event occurs. To evaluate the conditions with fresh data items, the frameworks acquire required data from IoT sensors. Since the data acquisition causes battery consumption of sensors, the frameworks should minimize the number of the data acquisition while keeping the sensor data fresh until finishing the condition evaluation. However, existing data acquisition schedulers inefficiently acquire sensor data because the schedulers assume each ECA rule in a program is independent of each other although different rules may share some sensing data from the same sensors. This work proposes an efficient sharing-aware data acquisition scheduling algorithm that reduces unnecessary data acquisition by sharing sensor data commonly used in different rules while satisfying time constraints. To evaluate the proposed scheduling algorithm, this work deploys 19 devices in an office, collects values of 26 different sensors for 144 hours, and simulates the proposed algorithm and a baseline algorithm. Compared to the baseline algorithm, the proposed algorithm reduces communication count and deadline miss ratio by 31.9% and 50.2% respectively.