Cost-Effective Sensor Data Collection from Internet-of-Things Zones Using Existing Transportation Fleets

Modern IoT devices are equipped with media-rich sensors that generate a heavy burden to local access networks. To improve the efficiency of data collection, we introduce the concept of "IoT zones" as geographically-correlated clusters of local IoT devices with well connected wireless networks that may have limited access to the Internet. We develop techniques to create a cost-effective data collection network using existing transportation fleets with predefined schedules to collect sensor data from IoT zones and upload them at locations with better network connectivity. Specifically, we provide solutions to the upload point placement and upload path planning problems given tradeoffs between collection quality, timing needs (QoS), and installation cost. We evaluate our approaches using a real-world bus network in Orange County, CA and study the applicability and efficiency of the proposed method as compared to several other approaches. The trace-driven simulations reveal that our best-performing algorithm: upload point selection (UPS) algorithm significantly outperforms others, e.g., in one of the scenarios with 160 total cost, it achieves sub-21 sec data transfer time (15+ times improvement), sub 3.2% late delivery ratio (about 12 times improvement), and above 96% data delivery ratio (about 50% improvement). In addition, it achieves the above performance without excessive installation cost: even when a cost limit of 640 is given, UPS algorithm opts for a solution with about 160 total cost (versus 640 from others).