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MPower statistics about devices that have currently installed MPower and are transmitted the collected data. Those statistics are taken from the official Google Play page.
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Due to their wide distribution and their growth in functionalities, mobile devices are the interaction point between users and the surrounding environment. Nevertheless, their resources are limited and variable over time, both in terms of performance and power. Especially when dealing with power consumption, mobile devices cannot disregard the envi...
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Citations
... While developing the BLUE-SENTINEL system, we had as a major concern the evaluation of the battery consumption of the iOS applications needed as an occupancy location sensors; this data is crucial to understand the feasibility of the system, since nowadays the energy consumption is the most remarkable constraint during the development of mobile devices applications [25,10]. Thanks to our previous experiences on this kind of devices [13,14], we have been able to identify as a possible source of inefficient behaviors in terms of power consumption, the Internet transmission of the data from the device to the server. ...
In the last years, the concept of smart buildings has been proposed and proved to be an effective solution to tackle the problem of reducing the power consumption of complex (both residential and commercial) buildings, while providing the users with a very high level of comfort. In this context, knowing the exact position of users inside the buildings has been identified as a needed feature to optimize the behavior of the building itself. Recently, using the occupants mobile devices as sensors has been validated as an effective solu- tion to have accurate occupancy detection systems, even if no energy efficient solution in therm of battery consumption has been found so far. On the contrary, with this work, we present BLUE-SENTINEL, an accurate and power effi- cient method to identify the occupants of each room of a smart building using mobile devices as source of informa- tion. The proposed approach faces the occupancy detection problem with a good accuracy by exploiting iBeacon, a very recent low-power technology proposed by Apple. In particu- lar, since the iBeacon protocol is built upon Bluetooth Low Energy (BLE), it represents a very highly power-efficient so- lution. In addition to this, the iBeacon technology is char- acterized by a good level of compatibility and portability, supporting both iOS- and Android-based devices. The pro- posed approach has been validated in a real environment with a prototype system released as open source showing how this technology is suitable for the occupancy detection in a smart building.
... As discussed in Section 2.2, the existing approaches that tackle these problems are either not accurate (e.g., too user centric) or not applicable in practice due to their limiting requirements (e.g., hardware based). We first highlighted the need for better approaches in [3], where we presented the concept of MPower, an adaptive power-modeling system-and Android application-capable of non-obtrusive power measurements and modeling. Approaches (and apps) that does both measurement and modeling on the device introduce an excessive power load, mainly due to the power model computation. ...
... To this end, we used our MPower Android application as a power sensing tool, following a crowdsourcing-like approach 2 . We presented the vision and the design of MPower in [3,19] and released the app on the Google Play Store in April, 2012. The MPower user interface is shown in Figure 1. ...
... This would ultimately result in high power leaks caused by MPower itself. According to our tests executed on different devices, and described in [3], revealed that a good tradeoff between data precision and the amount of power consumed by the collecting phase is around 10 seconds. In practice, we decreased the sampling rate until the MPower service permanently disappeared from the list of applications that contribute to more than the 2% of the overall power consumption. ...
Mobile devices have become the main interaction mean between users and the surrounding environment. An indirect measure of this trend is the increasing amount of security threats against mobile devices, which in turn created a demand for protection tools. Protection tools, unfortunately, add an additional burden for the smartphone's battery power, which is a precious resource. This observation motivates the need for smarter (security) applications, designed and capable of running within adaptive energy goals. Although this problem affects other areas, in the security area this research direction is referred to as "green security". In general, a fundamental need to the researches toward creating energy-aware applications, consist in having appropriate power models that capture the full dynamic of devices and users. This is not an easy task because of the highly dynamic environment and usage habits. In practice, this goal requires easy mechanisms to measure the power consumption and approaches to create accurate models. The existing approaches that tackle this problem are either not accurate or not applicable in practice due to their limiting requirements. We propose MPower, a power-sensing platform and adaptive power modeling platform for Android mobile devices. The MPower approach creates an adequate and precise knowledge base of the power "behavior" of several different devices and users, which allows us to create better device-centric power models that considers the main hardware components and how they contributed to the overall power consumption. In this paper we consolidate our perspective work on MPower by providing the implementation details and evaluation on 278 users and about 22.5 million power-related data. Also, we explain how MPower is useful in those scenarios where low-power, unobtrusive, accurate power modeling is necessary (e.g., green security applications).
... In this paper, we present MPower, the final implementation of the idea presented in [3], i.e., a system able to predict accurately an Android-based mobile device TTL, without any dedicated hardware tool or modification of the operating system. The model is focused on the hardware component power consumption, rather than users' behavior, allowing comparison among devices. ...
Nowadays, mobile devices are becoming more flexible and rich in functionalities. As already presented in [6] those devices are highly influenced by constraints, mainly regarding power management. In fact, mobile batteries are limited in time and there are no efficient methods able to manage power consumption. Even knowing the device Time To Live (TTL) is currently left to the user experience. In this paper, we presented MPower, a system able to predict the mobile device TTL, providing also the user with suggestions on the optimal device configuration w.r.t. the desired TTL. This allows the user to manage the available power resources, according to his/her needs, avoiding power wasting.
In the last years, the concept of smart buildings has been proposed and proved to be an effective solution to tackle the problem of reducing the power consumption of complex (both residential and commercial) buildings, while providing the users with a very high level of comfort. In this context, knowing the exact position of users inside the buildings has been identified as a needed feature to optimize the behavior of the building itself. Recently, using the occupants mobile devices as sensors has been validated as an effective solution to have accurate occupancy detection systems, even if no energy efficient solution in therm of battery consumption has been found so far. On the contrary, with this work, we present BLUE-SENTINEL, an accurate and power efficient method to identify the occupants of each room of a smart building using mobile devices as source of information. The proposed approach faces the occupancy detection problem with a good accuracy by exploiting iBeacon, a very recent low-power technology proposed by Apple. In particular, since the iBeacon protocol is built upon Bluetooth Low Energy (BLE), it represents a very highly power-efficient solution. In addition to this, the iBeacon technology is characterized by a good level of compatibility and portability, supporting both iOS- and Android-based devices. The proposed approach has been validated in a real environment with a prototype system released as open source showing how this technology is suitable for the occupancy detection in a smart building.
