ErdOS: An energy-aware social operating system for mobile handsets

IEICE Proceeding Series

Recently, user context information has been utilized for efficient and effective network and service management. In this situation, there is a strong need to collect user context information. One of the proprietary methods to collect user context information is to use sensors equipped inside a smartphone such as accelerometer, gyroscope, digital compass, and microphone. However, user context collection on a smartphone easily leads to rapid drain of the smartphone battery. To overcome the problem, this paper proposes a novel user context information collection model for efficient smartphone battery management. This model is based on two strategies: scheduling and dynamic sensor reconfiguration. The proposed model is implemented on Galaxy Nexus Android smartphone. The Performance evaluation shows that the proposed model reduces the energy consumption by a ratio of 42 percent compared to the current periodic sensor reading model.

Energy is an important resource in mobile computers now days. It is important to manage energy in efficient manner so that energy consumption will be reduced. Developers of operating system decided to increase the battery life time of mobile phones at operating system level. So, design of energy efficient mobile operating system is the best way to reduce the energy consumption in mobile devices. In this paper, currently used energy efficient mobile operating system is discussed and compared. Recent energy efficient techniques used to reduce the power consumption of mobile devices will also be summarized and discussed.

iaeme

In this investigation, an optimal approach for energy management is predicted under various operating conditions. This approach is considered as a pervasive computing solution to the energy problem in mobile devices. Its pervasiveness arises from the fact that communication is used and viewed as an opportunity to save energy, under all the operating conditions are outsourced from the mobile device on which they are executing, to a surrogate server machine with an infinite power source. As can be concluded, outsourcing code implies that the data involved in the computation will have to be transferred to and from the surrogate. This approach deviates from the traditional view of communication as a drain on the battery of a mobile device. The solution presented here is a compile-time solution and optimization, augmented by the necessary run-time support. The high-level source code is augmented by additional high-level code to intelligently (at run-time) allow the application running on the mobile device to outsource basic program blocks to a server. Both client and server applications are a byproduct of our approach, as the original source code is transformed into a client/server application where the client is installed on the mobile device and the server is stored on a surrogate machine. This approach uses a methodology from the domain of real-time systems to determine the number of loop iterations, and that facilitates a compile-time computing of executing each loop. also gathered the necessary information about the size of the data involved in each loop. This allows for determining the cost for outsourcing each loop (in terms of the cost for communicating the data to the surrogate). Once both metrics (computation energy cost, and communication energy cost) have been determined, It can easily generate a code that allows the application to make the run-time decision of whether outsourcing is beneficial.

2010 Ninth International Conference on Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR), 2010

Looking for optimizing the battery consumption is an open issue, and we think it is feasible if we analyze the battery consumption behavior of a typical context-aware application to reduce context-aware operations at runtime. This analysis is based on different context sensors configurations. Actually existing context-aware approaches are mainly based on collecting and sending context data to external components, without taking into account how expensive are these operations in terms of energy consumption. As a first result of our work in progress, we are proposing a way for reducing the context data publishing. We have designed a testing battery consumption architecture supported by Nokia Energy Profiler tool to verify consumption in different scenarios.

2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2013

Mobile phones play a pivotal role in supporting ubiquitous and unobtrusive sensing of human activities. However, maintaining a highly accurate record of a user's behavior throughout the day imposes significant energy demands on the phone's battery. In this paper, we present the design, implementation, and evaluation of METIS: an adaptive mobile sensing platform that efficiently supports social sensing applications. The platform implements a novel sensor task distribution scheme that dynamically decides whether to perform sensing on the phone or in the infrastructure, considering the energy consumption, accuracy, and mobility patterns of the user. By comparing the sensing distribution scheme with sensing performed solely on the phone or exclusively on the fixed remote sensors, we show, through benchmarks using real traces, that the opportunistic sensing distribution achieves over 60% and 40% energy savings, respectively. This is confirmed through a real world deployment in an office environment for over a month: we developed a social application over our frameworks, that is able to infer the collaborations and meetings of the users. In this setting the system preserves over 35% more battery life over pure phone sensing.

2014

Mobile phones play a pivotal role in supporting ubiquitous and unobtrusive sensing of human activities. However, maintaining a highly accurate record of a user's behavior throughout the day imposes significant energy demands on the phone's battery. In this work, we investigate a new approach that can lead to significant energy savings for mobile applications that require continuous sensing of social activities. This is achieved by opportunistically offloading sensing to sensors embedded in the environment, leveraging sensing that may be available in typical modern buildings (e.g., room occupancy sensors, RFID access control systems).

Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia - MoMM '12, 2012

Nowadays, there is a growing concern about the energy consumption of the ICT industry. This fact has given rise to a lot of energy saving research activities, which mainly focus on the hardware side of computational systems. However, it is tempting to suppose that only hardware dissipates power, not software. This paper discusses several software methods, which could be explored to develop energy-efficient mobile techniques. We argue that the development of applications that consider the energy saving, as one of their requirements, can result in a significant final energy saving because solutions will be part of the own software and they do not depend of external resources to obtain a lower consumption.

International Journal of Computer Network and Information Security

As the mobile devices are widely used in this world. With the increasing number of users, the numbers of customized applications are also introduced for these users according to their own requirements but on the other hand, there is a dire need of a system which must be energy conserved, estimated and maintained. A survey of energy consumption in mobile phones is presented in this paper with the factors at which the consumption of the energy depends on i.e. Energy consumed by OS, by hardware, by applications, by the user to interact with the applications, by wireless, by the sensor network. The energy management models and frameworks are also discussed in this paper.

2008

This paper argues for explicit consideration of data fidelity during development of context-aware systems. Increasing the amount of data captured, stored, and distributed does not always translate into into increased fidelity, and we can leverage this to avoid unnecessary energy overhead and increase device battery life. We introduce Context-Awareness Fidelity Expression (CAFE) as a framework for deliberately specifying application data fidelity adaptation with the aim of using context-awareness to reduce energy consumption.

Le Centre pour la Communication Scientifique Directe - HAL - Université de Nantes, 2018

A commonplace issue with portable technology is battery efficiency. While many industries are trying their best to improve battery life without sacrificing a product's quality and efficiency, we believe that further can be done to improve battery consumption on one's mobile device-from tablets to smartphones to laptops to everything else. Many applications on these devices are based on a microservice architecture. In this article, we introduce a new algorithm KaliGreen that can maneuver the microservices within a network of devices in order to maximize the run-time of a microservice-based application; moreover, KaliGreen allows a 54% increase in the average run-time of an application by shifting microservices from 6 devices (as example) with low battery or inefficient processing ratios to devices in better conditions. To achieve this, KaliGreen utilizes KaliMucho middleware, which is able manipulate microservices in run-time. This algorithm provides a plausible solution to maximizing energy consumption within a network of devices.