Efficient Distribution of Sensing Queries in Public Sensing Systems

Abstract

The advent of mobile phones paved the way for a new paradigm for gathering sensor data termed "Public Sensing" (PS). PS uses built-in sensors of mobile devices to opportunistically gather sensor data. For instance, the microphones that are available in a crowd of mobile phones can be used to capture sound samples, which can be used to construct a city noise map.A great challenge of PS is to reduce the energy consumption of mobile devices since otherwise users might not be willing to participate. One crucial part in the overall power consumption is the energy required for the communication between the mobile devices and the infrastructure. In particular, the communication required for sending sensing queries to mobile devices has been largely neglected in the related work so far.Therefore, in this paper, we address the problem of minimizing communication costs for the distribution of sensing queries. While existing systems simply broadcast sensing queries to all devices, we use a selective strategy by addressing only a subset of devices. In order not to negatively affect the quality of sensing w.r.t. completeness, this subset is carefully chosen based on a probabilistic sensing model that defines the probability of mobile devices to successfully perform a given sensing query.Our evaluations show that with our optimized sensing query distribution, the energy consumption can be reduced by more than 70% without significantly reducing the quality of sensing.

GraMap: QoS-Aware Indoor Mapping Through Crowd-Sensing Point Clouds with Grammar Support

“…In terms of energy efficiency in crowd-sensing, we developed a quality model to probabilistically assess the sensing capabilities of mobile users while distributing the sensing queries [3]. The model defines the probability that a mobile device satisfies a given sensing query.…”

“…(2) Important QoS metrics, such as energy overhead on the participating mobile devices as well as the sensing latency, have been broadly overlooked. (3) The collected data types are limited to the derivation of 2D maps. To tackle such challenges, we introduce GraMap as an indoor modeling method through crowd-sensing 3D point clouds in a participatory fashion.…”

“…(2) We present an approach that exploits structural knowledge encoded in formal grammars to compensate for inaccuracies due to collecting point clouds from the crowd. (3) We introduce a probabilistic quality model to avoid wasting energy due to uploading several low-quality point clouds of the same geographical region. (4) We provide an implementation of indoor grammars describing structural information about room layouts.…”

On Architectural Qualities and Tactics for Mobile Sensing

“…Denied requests can be served by, e.g., caching previous responses, and aggregating several requests. Examples: Baier et al[2] propose to limit sensing service requests for public sensing systems by only requesting services from those mobile devices that are most likely to return valuable sensor data. PVC ProvidercentricIf entities serving requests are the main trigger of data sharing actions (e.g., in continuous sensing scenarios) energy efficiency can be improved by limiting response rates of request-serving entities.…”