SenseLE: Exploiting Spatial Locality in Decentralized Sensing Environments

Abstract

Generally, smart devices, such as smartphones, smartwatches, or fitness trackers, communicate with each other indirectly, via cloud data centers. Sharing sensor data with a cloud data center as intermediary invokes transmission methods with high battery costs, such as 4G LTE or WiFi. By sharing sensor information locally and without intermediaries, we can use other transmission methods with low energy cost, such as Bluetooth or BLE. In this paper, we introduce Sense Low Energy (SenseLE), a decentralized sensing framework which exploits the spatial locality of nearby sensors to save energy in Internet-of-Things (IoT) environments. We demonstrate the usability of SenseLE by building a real-life application for estimating waiting times at queues. Furthermore, we evaluate the performance and resource utilization of our SenseLE Android implementation for different sensing scenarios. Our empirical evaluation shows that by exploiting spatial locality, SenseLE is able to reduce application response times (latency) by up to 74% and energy consumption by up to 56%.

RideMatcher: Peer-to-Peer Matching of Passengers for Efficient Ridesharing

“…For example, Bluetooth 5 devices are now able to transfer data at 50 Mbit/s over distances up to 240 meters [8]. Also, our earlier research [9] proves the usability of Bluetooth Low Energy in remote sensing scenarios. Here, we combine these techniques with a fog-based peer-to-peer gossiping protocol in order to build a reliable mesh network in which users can find ridesharing partners.…”