PSense: Reducing Energy Consumption in Public Sensing Systems

Abstract

Utilizing peoples' mobile devices for gathering sensor data has attracted a lot of attention within the last few years. As a result, a great variety of systems for sensing environmental phenomena like temperature or noise have been proposed. However, most of these systems do not take into account that mobile devices have only limited energy resources. For instance, an often assumed prerequisite is that mobile devices are always aware of their position. Given the fact that a position fix is a very energy consuming operation, continuous positioning would quickly drain a device's battery. Since the owners of the mobile devices will not tolerate a significant reduction of the devices' battery lifetime, such an approach is not suitable. To address this issue we present PSense, a flexible system for efficiently gathering sensor data with mobile devices. By avoiding unnecessary position fixes, PSense reduces the energy consumption of mobile devices by up to 70% compared to existing mobile sensing approaches. This is achieved by introducing an adaptive positioning mechanism and by utilizing energy efficient short-range communication to exchange position related information.

A novel localization and coverage framework for real-time participatory urban monitoring

“…However, the major limitation of these work is that the position fix by location sensor and sending location update to the server can introduce significant energy overhead on the mobile devices, especially when the data collection frequency is high. PSense [24] was proposed to reduce unnecessary position fixes by GPS to save energy but still it spends a lot of energy by sending location updates to the server. Besides these two major approaches, some work also suggested to apply an ad hoc networking assisted crowdsourcing [26,44,46].…”

“…For WSNs, energy efficiency in the data collection process has been widely investigated from different aspects [18][19][20][21]. Similar to wireless sensors, the mobile devices are battery powered and hence energy efficiency in the data collection process is also very important [22][23][24]. The existing approaches that address these challenges broadly fall into two categories.…”

QoI-aware energy-efficient participant selection

“…Step 2 -Select one participant at a time from ℬ to : for each participant in ℬ, his/her efficiency ( , ) is calculated as in (14), and the most efficiency participant, denoted as arg max ( , ), is selected each round and is moved from ℬ to , i.e., arg max ( ) = ( , ), ∀ ∈ ℬ.…”

“…In [13], the authors thoroughly investigate the energy consumption of smartphone sensors, and draw the conclusion that the their energy consumption are almost identical. [14], [15] largely improved the device battery lifetime by inferring the sensor readings by energycheaper sensors or temporal continuous readings, since the values of various context attributes can be highly correlated. [16] proposed several minimum energy sensing scheduling algorithms, to tackle the problem of collaborative sensing in specific scenario of road segment.…”

“…Although many different participant selection strategies have been proposed, they either only consider the participants' trajectory known as a priori and its fitness to the task's sensing region [4], [5], [6], [7], [8], [9], [10], [11], [12], or only consider the energy efficiency in collaborative sensing by reducing individual participant's sensing frequency, replacing high energy-cost sensors with low energy-cost sensors in specific application scenarios [13], [14], [15], [16]. Different from all these, the aim of this paper is to find an optimal subset of participants to contribute sensory data that can best satisfy QoI requirements of a task in an energy-efficient way.…”