Task-driven Data Offloading for Fog-enabled Urban IoT Services

Past years have witnessed the rapid increasing number of smart devices and objects deployed in the urban environment. Leveraging helpful data generated by hundreds of millions of smart objects, a large number of services in the Internet of Things (IoT) are devised and developed to improve our urban life quality. However, uploading the unprecedented volume of sensing data from IoT sensors to the cloud directly can lead to huge unnecessary consumption and hurt the quality of IoT services. This work leverages the fog architecture to devise a task-driven data offloading (TDO) algorithm in urban IoT services. Specifically, a three-layer urban IoT service architecture is proposed, and the TDO process is formulated as a combination optimization problem taking task deadlines and abilities of fog devices into consideration. Then, we prove the TDO problem is NP-hard, and the G-TDO algorithm is devised to solve it with a careful designed utility function. Also, we propose RG-TDO algorithm to improve the G-TDO algorithm considering the overlaps of tasks. Finally, we demonstrate the significant performance of the proposed algorithms with extensive evaluations based on real-world data set.