Figure 1 - uploaded by Alessandro Antonio Nacci
Content may be subject to copyright.
Source publication
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 (T...
Contexts in source publication
Context 1
... relies on a system architecture based on two main components: an Android client logging application, running on the mobile device, and a remote server, for the data storage and the model computation. In Fig. 1 the information flow is presented. At first, the application on the mobile device gathers data related to energy consumption, e.g., battery level, CPU frequency, screen brightness, and sends them to a remote cloud server, responsible for the power model generation. Finally, the model is sent back to the device and the currently ...
Context 2
... devices, e.g., smartphones, PDAs and tablets, increase in number of functionalities in the recent years, making them appealing to a wide range of users. Thus, even if they are becoming more and more powerful in term of supported computation, they are constrained w.r.t. the power consumption. In fact, batteries are limited in time and their Time To Live (TTL) is highly influenced by the current phone state, the user usage profile and external conditions, e.g., signal strength. As a consequence, since these devices are subject to sudden and unpredictable changes of their conditions, a method for computing the device TTL is necessary. A practical way of predicting the TTL is to model the battery discharging curve, since TTL coincides with the time the battery level reaches zero level. Android OS [1], one of the most popular mobile OS, only provides a percentage as remaining power indicator, leaving to the user experience and knowledge to determine the TTL of its mobile device. In order to know a TTL, the discharge curve is needed, and consequently, a power mobile of the device. Literature on mobile power consumption estimation provides a wide range of works about power modeling [6]. Most of them use benchmark applications on a single device to gather data, providing model which hardly generalize to the burden of devices currently available. Recently, the need for considering users’ behaviour is highlighted [7]. A first attempt in this direction has been done by Kang et al. [4], where real data are used to build a personalized power model. They do not take into account power consumption specific to mobile device, which can behave differently even with a given user profile. 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. The whole architecture is based on a mobile application implementing a low power logger and a remote server, where the main computations are offloaded. MPower relies on a system architecture based on two main components: an Android client logging application, running on the mobile device, and a remote server, for the data storage and the model computation. In Fig. 1 the information flow is presented. At first, the application on the mobile device gathers data related to energy consumption, e.g., battery level, CPU frequency, screen brightness, and sends them to a remote cloud server, responsible for the power model generation. Finally, the model is sent back to the device and the currently estimated TTL is shown by the MPower client. The power consumption model estimation phase is performed on data gathered by the MPower Android app: no controlled environment experiments are required. MPower relies only on the everyday usage data, providing a flexible system, adaptive w.r.t. new devices and new OS versions and even able to handle battery degradation effects. Data gathered from the device may be grouped into two main categories: controllable actuators values and uncontrollable variables. The first group determines the device configurations : each assignment of actuators values determines device state. For instance, a configuration is WiFi OFF, 3G ON, bluetooth OFF, etc. Given a configuration, the uncontrollable variables, such as time elapsed, CPU usage, etc., are used as input for the TTL prediction. For each configuration, a power consumption model is computed, estimating an ARX model [5]. The power model is sent to the device as a look-up-table , containing TTLs corresponding to each configuration. Thus, the mobile device does not have to perform any further computation, but a simple query on the look-up table. A preliminary version of MPower app is available on the Google Play store [2] and the complete version will be released in September 1 . Currently, it logs the phone information and allows the user to visualize the phone usage statistics. In the final version it will provide also the TTL in the current configuration and will offer suggestion for achieving the desired TTL by changing configuration. Fig. 2(a) presents the application main page screenshot: the battery TTL for the current configuration is reported in the center of the screen, while on the lower part it is possible to access phone statistics (buttons “Reports” and “Charts”) and to allow the phone to refine the model if needed (button “Collecting new data”). The button “Set battery life” leads to the page in Fig. 2(b), where the user can change the configuration, to modify TTL. Moreover, the user can use the available filters to set constraints on functionalities the device must provide. Fig. 3 shows the third MPower feature: the visualization of the device usage statistics. Specifically, Fig. 3(a) reports a pie-chart with the different networks data usage distribution, while Fig. 3(b) visualize the overall network data usage and the power consumption. MPower functionalities allows the user to keep the battery consumption under control, without having an excessive overhead in power consumption. In fact, since it runs continuously on the mobile device, a low impact on the battery is required. For this reason, to design a non-power-hungry application, the following aspects have been addressed during the application developing ...
Similar publications
Vein detection is an important issue for the medical field. There are some commercial devices for detecting veins using infrared radiation. However, most of these commercial solutions are cost-prohibitive. Recently, veins detection has attracted much attention from research teams. The main focus is on developing real-time systems with low-cost hard...
In open mobile environments, mobile device may be connected to several network system offering heterogeneous sets of services using different names. The services can be anything and introduced by anybody, which results to have the possibility of having similar service with different names or different services having the same name. The current IEEE...
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,...
In this paper the planning and development of a backend software for a Electronic Health Record (eHR) Management System is presented. The system is based on the well-known Google's Android open platform, that offers easy networking procedures and low design complexity. The aim of this work is to present the system's client, which operates on a mobi...
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. For this reason, we performed three different experiments: a first one to test the baseline energy consumption (when the app is not installed), a second one to test the overall energy consumption (normal app behavior) and a third one to test the HTTP over WiFi communication energy consumption (app with the HTTP communications disabled). ...
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.
... Alongside with this application, it was necessary to record the status of the device, e.g., to record the remaining battery percentage, the CPU frequency and so on. For this purpose, we used the MPower application available on the Google Play Store [18]. ...
In the context of mobile devices, the thermal prob- lem is an emerging one, as it affects the user experience and involves factors that are both internal and external with respect to the device. In this paper, we present an evaluation of these factors, that consists of two parts. The first one is the analysis of thermal interactions between the internal components of the system, performed with an infrared camera. The second part consists in the analysis of the impact of external temperature on the performance of CPUs and batteries. We finally propose the VirtIRCamera app, a thermal simulator for Android devices, able to generate thermal maps relying on the thermal model proposed within this paper. As a characterization of the thermal phenomenon, this work is the first step in the creation of thermal management techniques that are specifically designed for mobile devices.
... Moreover, making a deep state of the art analysis 1 it is possible to notice that on the last few year there is a lack of effort in creating new cryptographic implementations of the existing algorithms even if, the technological scenario is deeply changed. In fact, on the one hand there is a big requirement of low energy and low area implementations for mobile devices [10] while on the other hand there is a big requirement of high throughput solutions for the application of the big data era [16]. ...
Although the reliability and robustness of the AES protocol have been deeply proved through the years, recent research results and technology advancements are rising serious concerns about its solidity in the (quite near) future. In this context, we are proposing an extension of the AES algorithm in order to support longer encryption keys (thus increasing the security of the algorithm itself). In addition to this, we are proposing a set of parametric implementations of this novel extended protocols. These architectures can be optimized either to minimize the area usage or to maximize their performance. Experimental results show that, while the proposed implementations achieve a throughput higher than most of the state-of-the-art approaches and the highest value of the Performance/Area metric when working with 128-bit encryption keys, they can achieve a 84x throughput speed-up when compared to the approaches that can be found in literature working with 512-bit encryption keys.
... Approaches (and apps) that does both measurement and modeling on the device introduce an excessive power load, mainly due to the power model computation. As we discussed in [8], on the one hand an accurate power model requires expensive computation, on the other hand, the short battery life makes this unfeasible in practice. ...
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 the last few years, multi-core processors entered into the domain of embedded systems: this, together with virtualization techniques, allows multiple applications to easily run on the same System-on-Chip (SoC). As power consumption remains one of the most impacting costs on any digital system, several approaches have been explored in literature to cope with power caps, trying to maximize the performance of the hosted applications. In this paper, we present some preliminary results and opportunities towards a performance-aware power capping orchestrator for the Xen hypervisor. The proposed solution, called XeMPUPiL, uses the Intel Running Average Power Limit (RAPL) hardware interface to set a strict limit on the processor's power consumption, while a software-level Observe-Decide-Act (ODA) loop performs an exploration of the available resource allocations to find the most power efficient one for the running workload. We show how XeMPUPiL is able to achieve higher performance under different power caps for almost all the different classes of benchmarks analyzed (e.g., CPU-, memory-and IO-bound).
Autonomicity is a golden feature when dealing with a high level of complexity. This complexity can be tackled partitioning huge systems in small autonomous modules, i.e., agents. Each agent then needs to be capable of extracting knowledge from its environment and to learn from it, in order to fulfill its goals: this could not be achieved without proper modeling techniques that allow each agent to gaze beyond its sensors. Unfortunately, the simplicity of agents and the complexity of modeling do not fit together, thus demanding for a third party to bridge the gap.
Given the opportunities in the field, the main contributions of this work are twofold: (1) we propose a general methodology to model resource consumption trends and (2) we implemented it into MARC, a Cloud-service platform that produces Models-as-a-Service, thus relieving self-aware agents from the burden of building their custom modeling framework. In order to validate the proposed methodology, we set up a custom simulator to generate a wide spectrum of controlled traces: this allowed us to verify the correctness of our framework from a general and comprehensive point of view.
With the increasing popularity of smartphones, battery life is among the most crucial issues for mobile users. This paper presents a crowdsensing-based use guide to extend the lifetime of smartphones. The system answers a question raised by phone usage: Why is my phone battery draining quickly compared to others phones despite running the same applications? The proposed system pinpoints the major causes of battery drain in terms of both hardware and software aspects. In relation to the hardware aspect, the system quantifies degree of battery aging as a ratio metric; an estimate of 50% indicates that the battery is at half of full capacity, meaning that battery usage time is approximately half that of a new battery. The system automatically profiles battery age based on charging duration data collected by crowdsensing. In its software aspect, the system guides phone configuration to extend application usage times. The system mines large-scale usage data to infer the major energy holes in a user's phone usage. The scheme works autonomously without user intervention and does not require any external equipment. Extensive evaluation with 3,000 users demonstrated that the proposed scheme successfully extends battery life for typical mobile users.
