Cutting Wi-Fi Scan Tax for Smart Devices

Today most popular mobile apps and location-based services require near always-on Wi-Fi connectivity (e.g., Skype, Viber, Wi-Fi Finder). The Wi-Fi power drain resulting from frequent Wi-Fi active scans is undermining the battery performance of smart devices and causing users to remove apps or disable important services. We collectively call this the scan tax problem. The main reason for this problem is that the main processor has to be active during Wi-Fi active scans and hence consumes a significant and disproportionate amount of energy during scan periods. We propose a simple and effective architectural change, where the main processor periodically computes an SSID list and scan parameters (i.e. scan interval, timeout) taking into account user mobility and behavior (e.g. walking); allowing scan to be offloaded to the Wi-Fi radio. We design WiScan, a complete system to realize scan offloading, and implement our system on the Nexus 5. Both our prototype experiments and trace-driven emulations demonstrate that WiScan achieves 90%+ of the maximal connectivity (connectivity that the existing Wi-Fi scan mechanism could achieve with 5 seconds scan interval), while saving 50-62% energy for seeking connectivity (the ratio between the Wi-Fi connected duration and total time duration) compared to existing active scan implementations. We argue that our proposed shift not only significantly reduces the scan tax paid by users, but also ultimately leads to ultra-low power, always-on Wi-Fi connectivity enabling a new class of context-aware apps to emerge

Sensor Integration for Smart Cities Using Multi-Hop Networks

Smart Cities are designed to be living systems and turn urban dwellers life more comfortable and interactive by keeping them aware of what surrounds them, while leaving a greener footprint. The Future Cities Project [1] aims to create infrastructures for research in smart cities including a vehicular network, the BusNet, and an environmental sensor platform, the Urban Sense. Vehicles within the BusNet are equipped with On Board Units (OBUs) that offer free Wi-Fi to passengers and devices near the street. The Urban Sense platform is composed by a set of Data Collection Units (DCUs) that include a set of sensors measuring environmental parameters such as air pollution, meteorology and noise. The Urban Sense platform is expanding and receptive to add new sensors to the platform. The parnership with companies like TNL were made and the need to monitor garbage street containers emerged as air pollution prevention. If refuse collection companies know prior to the refuse collection which route is the best to collect the maximum amount of garbage with the shortest path, they can reduce costs and pollution levels are lower, leaving behind a greener footprint. This dissertation work arises in the need to monitor the garbage street containers and integrate these sensors into an Urban Sense DCU. Due to the remote locations of the garbage street containers, a network extension to the vehicular network had to be created. This dissertation work also focus on the Multi-hop network designed to extend the vehicular network coverage area to the remote garbage street containers. In locations where garbage street containers have access to the vehicular network, Roadside Units (RSUs) or Access Points (APs), the Multi-hop network serves has a redundant path to send the data collected from DCUs to the Urban Sense cloud database. To plan this highly dynamic network, the Wi-Fi Planner Tool was developed. This tool allowed taking measurements on the field that led to an optimized location of the Multi-hop network nodes with the use of radio propagation models. This tool also allowed rendering a temperature-map style overlay for Google Earth [2] application. For the DCU for garbage street containers the parner company provided the access to a HUB (device that communicates with the sensor inside the garbage containers). The Future Cities use the Raspberry pi as a platform for the DCUs. To collect the data from the HUB a RS485 to RS232 converter was used at the physical level and the Modbus protocol at the application level. To determine the location and status of the vehicles whinin the vehicular network a TCP Server was developed. This application was developed for the OBUs providing the vehicle Global Positioning System (GPS) location as well as information of when the vehicle is stopped, moving, on idle or even its slope. To implement the Multi-hop network on the field some scripts were developed such as pingLED and “shark”. These scripts helped upon node deployment on the field as well as to perform all the tests on the network. Two setups were implemented on the field, an urban setup was implemented for a Multi-hop network coverage survey and a sub-urban setup was implemented to test the Multi-hop network routing protocols, Optimized Link State Routing Protocol (OLSR) and Babel