Researchers have leveraged modern day sensor rich smartphones to collect crowdsourced data and analyse it for different phenomenon of interests. Crowdsensing applications must bear with the limited energy, sensing, and computational resources available on the phone. To relief some burden from the smartphones, the authors envision a new crowdsourcing architecture where low‐cost, low‐energy sensors embedded in objects around us, i.e. walls, traffic lights, and billboards, providing a variety of sensors depending on their context, e.g. AirQuality, temperature. These devices would carry on the sensing, processing, and broadcasting of sensor data using wireless interfaces. Smartphones on the other hand, would opportunistically discover and collect data from these devices to provide a much better, richer, and energy‐efficient sensing infrastructure. They discuss the usage of Bluetooth low energy (BLE) as a new energy‐efficient sensing resource for crowdsensing. They focus on defining a unified BLE sensing framework, which provides a number of smart sensing schemes to ease the development of energy efficient and context aware crowdsensing applications. They also provide an open‐source library that allows developers to utilise this framework and acts as a layer to support opportunistic crowdsensing out of the box.

Current outdoor localization techniques fail to provide the required accuracy for estimating the car's lane. In this paper, we present LaneQuest: a system that leverages the ubiquitous and low-energy inertial sensors available in commodity smart-phones to provide an accurate estimate of the car's current lane. LaneQuest leverages hints from the phone sensors about the surrounding environment to detect the car's lane. For example, a car making a right turn most probably will be in the right-most lane, a car passing by a pothole will be in a specific lane, and the car's angular velocity when driving through a curve reflects its lane. Our investigation shows that there are amble opportunities in the environment, i.e. lane "anchors", that provide cues about the car's lane. To handle the ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane-span distribution of the different lane-level anchors. Our evaluation results from implementation on different android devices and 260Km driving traces by 13 drivers in different cities shows that LaneQuest can detect the different lane-level anchors with an average precision and recall of more than 90%. This leads to an accurate detection of the exact car's lane position 80% of the time, increasing to 89% of the time to within one lane. This comes with a low-energy footprint, allowing LaneQuest to be implemented on the energy-constrained mobile devices.

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.