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Morphone.OS: Context-Awareness in Everyday Life
Abstract and Figures
Mobile devices, due to their wide distribution and to their increasing smartness and availability of computational power, can become the interaction point between users and their surrounding environments. However, current mobile devices OSes lack of the ability to anticipate and overcome internal and external changes. Integrating mechanisms of self-awareness and self-adaptability in nowadays smartphones is an attractive perspective to match with these requirements. Moreover, adaptive behaviors can enhance the management by the mobile device itself, of the available resources at its best, e.g., the battery life. This paper envisions various situations in which a self-aware mobile device can interact with the surrounding environment and support the user in performing everyday actions. A prototype of such an adaptive device, called morphone.os and based on the Android OS, has been designed and implemented to verify the reaction of the device in different situations providing convincing and promising preliminary results.
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... When a mobile application is running, the energy consumption not only depends on the installed code, but also on how the user interacts with the application [3] . Based on the user's interaction, the application functionality uses the available resources differently. ...
... Context This module maintains the information about the current application context. The contextual information is divided into three submodules [3] : (1) Environment represents the external information of the application (e.g.,location); (2) App Status maintains the application state (user behavior) between executions by using the information provided by Monitors ; and (3) Device Status gets the state about the mobile itself (e.g., battery level). ...
... In mobile applications, despite the fact that adaptations are frequently driven by non-functional requirements such as improving the application's performance [10,12,15] , increasing the failure tolerance [15] , or improving the user experience [3,32,48] , among others; reducing the energy consumption is the most important objective [3,10,45,46,49,53] , as the most recent energy optimization approaches focus on [10,50,53,54] . A dynamic adaptation system is characterized by different dimensions or characteristics such as the primary reconfiguration goal (e.g., improving performance, energy saving), the type of adaptation engine (application-oriented vs system-oriented) [10,13] , or the context that is monitored (e.g., device status, environment, user's interactions) [3] . ...
Context The energy consumption of mobile devices is increasing due to the improvement in their components (e.g., better processors, larger screens). Although the hardware consumes the energy, the software is responsible for managing hardware resources such as the camera software and its functionality, and therefore, affects the energy consumption. Energy consumption not only depends on the installed code, but also on the execution context (environment, devices status) and how the user interacts with the application.
Objective In order to reduce the energy consumption based on user behavior, it is necessary to dynamically adapt the application. However, the adaptation mechanism also consumes a certain amount of energy in itself, which may lead to an important increase in the energy expenditure of the application in comparison with the benefits of the adaptation. Therefore, this footprint must be measured and compared with the benefit obtained.
Method In this paper, we (1) determine the benefits, in terms of energy consumption, of dynamically adapting mobile applications, based on user behavior; and (2) advocate the most energy-efficient adaptation mechanism. We provide four different implementations of a proposed adaptation model and measure their energy consumption.
Results The proposed adaptation engines do not increase the energy consumption when compared to the benefits of the adaptation, which can reduce the energy consumption by up to 20%.
Conclusion The adaptation engines proposed in this paper can decrease the energy consumption of the mobile devices based on user behavior. The overhead introduced by the adaptation engines is negligible in comparison with the benefits obtained by the adaptation.
... Designing such platforms is really challenging since, on the one hand, they need to be small (in order to be portable) while, on the other hand, they need to embed a lot of different electronic components (i.e., a large variety of sensors and communication interfaces [2]) to assist the user during his daily routine. As a consequence, managing thermal factors of such devices is crucial: as first, there is no space for fans or cooling mechanisms in a system where many critical components (e.g., the battery, the radio communication elements, the processor, etc.) generate a significant quantity of heat; secondly, the same external temperature highly influences the behaviour of such devices [3]. These aspects are the factors that increase the relevance of the thermal problem on mobile devices: it directly harms the user experience, as these devices are meant to be kept in a hand, while high temperatures may damage the electronic components of the device itself too [4]. ...
... If compared to traditional computing platforms, smartphones and tablets are exposed to a wider variety of external environmental conditions [3]. It is then interesting to study if and how the external temperature can influence their internal status and consequently their performance, laying the foundation for future thermal management systems for mobile devices. ...
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.
... Generalizing the examples, we inferred that the idea of context aware computing is strictly related with the so called Observe, Decide and Act (ODA) loop [8]. In fact, everytime we want to implement such a system, we need an observation phase on the environment, a decision phase to analyze data gathered in order to choose which are the actions to be performed, and finally an action phase to actually perform those actions. ...
... We introduced the context awareness at the OS level in a previous work, [8]. Anyway, nowadays, everyone has a mobile device with a pre-loaded OS and generally applications can be easily downloaded form a marketplace. ...
Mobile devices take an important part in everyday life. They are now cheaper and widespread, but still a lot of time is spent by the users to configure them: users adapt to their own device, not vice versa. Can our smartphones do something smarter? In this work, we propose a framework to support the development of context aware applications for Android devices: the goal of such applications is to reduce as much as possible the interaction with the user, making use of automatic and intelligent components. Moreover, these components should consume as less power and computational resources as possible, being them part of a mobile ecosystem whose battery and hardware are highly constrained. The work implies the study of a methodology that fits the Android framework and the design of a highly extensible software architecture. An open source framework based on the proposed methodology is then described. Some use cases are finally presented, analyzing the performances and the limitations of the proposed methodology.
... In addition to advances in new hardware, the installed software itself also increasingly requires a larger battery capacity so that the user can obtain good interaction with the installed application without having to worry about recharging the battery more often than previously necessary [Nacci et al. 2013]. And for this, resources such as GPS, speech recognition and notification systems often end up being used in an excessively unnecessary way [Cañete et al. 2020]. ...
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).
Adaptive, or self-aware, computing has been proposed to help application programmers confront the growing complexity of multicore software development. However, existing approaches to adaptive systems are largely ad hoc and often do not manage to incorporate the true performance goals of the applications they are designed to support. This paper presents an enabling technology for adaptive computing systems: Application Heartbeats. The Application Heartbeats framework provides a simple, standard programming interface that applications can use to indicate their performance and system software (and hardware) can use to query an application's performance. The PARSEC benchmark suite is instrumented with Application Heartbeats to show the broad applicability of the interface and an external resource scheduler demonstrates the use of the interface by assigning cores to an application to maintain a designated performance goal.
Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.
Multi-sensor management concerns the control of environment perception activities by managing or coordinating the usage of multiple sensor resources. It is an emerging research area, which has become increasingly important in research and development of modern multi-sensor systems. This paper presents a comprehensive review of multi-sensor management in relation to multi-sensor information fusion, describing its place and role in the larger context, generalizing main problems from existing application needs, and highlighting problem solving methodologies.
This paper describes PowerBooter, an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation. PowerBooter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants, which each have different power consumption properties and therefore require different power models. PowerTutor is intended to ease the design and selection of power efficient software for embedded systems. Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.
Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-* properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how , what , when and where , towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.
Recently commercial mobile phones have been shipped with integrated Wi-Fi NIC (Network Interface Card), while a fundamental barrier for easily using such Wi-Fi is its high energy cost for phone. We profile two integrated mobile phone Wi-Fi NICs and observe that the energy cost on PSM (Power Saving Mode) is greatly reduced, yet the scan state still costs most of the energy when it's not connected on discovering potential AP (Access Point). In this paper, we propose footprint, leveraging the cellular information such as the overheard cellular tower IDs and signal strength, to guild the Wi-Fi scan and thus greatly reduce the number of unnecessary scans through the changes and history logs of the mobile user's locations. Our experiments and implementation demonstrate that our scheme effectively saves energy for mobile phone integrated with Wi-Fi.
As the edge of the Internet becomes increasingly mobile, location-based Internet applications have taken on a much more prominent role. At the same time, though, the underlying geolocation systems that support these applications were designed with specific circumstances in mind, rather than being general to the whole Internet. The IETF GEOPRIV architecture provides a unified geolocation system for the Internet, allowing applications to benefit from current geolocation design patterns as well as some new security and privacy features.
The most powerful trend in mobile computing is that of increased online social activity. To meet users' future expectations, mobile computing researchers must consider current limitations and identify upcoming challenges.
In this paper, we present a measurement study of the energy con- sumption characteristics of three widespread mobile networking technologies: 3G, GSM, and WiFi. We find that 3G and GSM in- cur a high tail energy overhead because of lingering in high power states after completing a transfer. Based on these measurements, we develop a model for the energy consumed by network activity for each technology. Using this model, we develop TailEnder, a protocol that reduces energy consumption of common mobile applications. For appli- cations that can tolerate a small delay such as e-mail, TailEnder schedules transfers so as to minimize the cumulative energy con- sumed while meeting user-specified deadlines. We show that the TailEnder scheduling algorithm is within a factor 2◊ of the optimal and show that any online algorithm can at best be within a factor 1.62◊ of the optimal. For applications like web search that can benefit from prefetching, TailEnder aggressively prefetches several times more data and improves user-specified response times while consuming less energy. We evaluate the benefits of TailEnder for three different case study applications—email, news feeds, and web search—based on real user logs and show significant reduction in energy consumption in each case. Experiments conducted on the mobile phone show that TailEnder can download 60% more news feed updates and download search results for more than 50% of web queries, compared to using the default policy.