Symbiotic Sensing for Energy-Intensive Tasks in Large-Scale Mobile Sensing Applications

Abstract

Energy consumption is a critical performance and user experience metric when developing mobile sensing applications, especially with the significantly growing number of sensing applications in recent years. As proposed a decade ago when mobile applications were still not popular and most mobile operating systems were single-tasking, conventional sensing paradigms such as opportunistic sensing and participatory sensing do not explore the relationship among concurrent applications for energy-intensive tasks. In this paper, inspired by social relationships among living creatures in nature, we propose a symbiotic sensing paradigm that can conserve energy, while maintaining equivalent performance to existing paradigms. The key idea is that sensing applications should cooperatively perform common tasks to avoid acquiring the same resources multiple times. By doing so, this sensing paradigm executes sensing tasks with very little extra resource consumption and, consequently, extends battery life. To evaluate and compare the symbiotic sensing paradigm with the existing ones, we develop mathematical models in terms of the completion probability and estimated energy consumption. The quantitative evaluation results using various parameters obtained from real datasets indicate that symbiotic sensing performs better than opportunistic sensing and participatory sensing in large-scale sensing applications, such as road condition monitoring, air pollution monitoring, and city noise monitoring.

Do Monetary Incentives Influence Users’ Behavior in Participatory Sensing?

“…Two variants of mobile crowd sensing are generally discerned: opportunistic sensing, in which the explicit participation of the device owner is not required [ 2 , 3 , 4 ], and participatory sensing, which is the focus of this article, in which participants explicitly contribute sensing data [ 5 , 6 ]. Due to the proliferation of mobile devices and the active involvement of the participants, participatory sensing applications are able to collect information in a variety of fields [ 7 , 8 ] with high spatial and temporal resolution. For instance, in Smart Cities, we can find participatory sensing applications that measure noise [ 9 , 10 , 11 ], noise with subjective feedback [ 12 ], air pollution [ 13 , 14 , 15 ], road and traffic condition [ 16 , 17 ], structural health monitoring [ 18 ] and cellular signal strength [ 19 ].…”

Smartphone Crowdsourcing and Data Sharing Towards Advancing User Experience and Mobile Services

“…We are witnessing an era where mobile devices, like smartphones and tablets, with numerous integrated sensors enable the low-cost recording, storage and transmission of real time data [11] regarding the environment of their owners. With that being said, the idea of transforming mobile devices into sensors grew to be quite appealing, which unavoidably gave birth to the concepts of participatory and mobile crowd sensing [12,13].…”

Poaching Detection Technologies—A Survey

“…The harsh environment and potential tampering by poachers will cause components of an APS to fail; thus, a high resilience is demanded. Distributed systems have proven to be very resilient because they are capable of self organizing [ 87 ]. In other words, when a component fails, the system can automatically reorganize itself.…”