Smartphone Battery Research Articles

Particle Swarm Optimization Using Adaptive Boundary Correction for Human Activity Recognition

As a kind of personal lifelog data, activity data have been considered as one of the most compelling information to understand the users habits and to calibrate diagnoses. In this paper, we proposed a robust algorithm to sampling rates for human activity recognition, which identifies a user’s activity using accelerations from a triaxial accelerometer in a smartphone. Although a high sampling rate is required for high accuracy, it is not desirable for actual smartphone usage, battery consumption, or storage occupancy. Activity recognitions with well-known algorithms, including MLP, C4.5, or SVM, suffer from a loss of accuracy when a sampling rate of accelerometers decreases. Thus, we start from particle swarm optimization (PSO), which has relatively better tolerance to declines in sampling rates, and we propose PSO with an adaptive boundary correction (ABC) approach. PSO with ABC is tolerant of various sampling rate in that it identifies all data by adjusting the classification boundaries of each activity. The experimental results show that PSO with ABC has better tolerance to changes of sampling rates of an accelerometer than PSO without ABC and other methods. In particular, PSO with ABC is 6%, 25%, and 35% better than PSO without ABC for sitting, standing, and walking, respectively, at a sampling period of 32 seconds. PSO with ABC is the only algo-rithm that guarantees at least 80% accuracy for every activity at a sampling period of smaller than or equal to 8 seconds.

SMoW: An Energy-Bandwidth Aware Web Browsing Technique for Smartphones

With the rapid advancement of mobile devices, people have become more attached to them than ever. This rapid growth combined with millions of applications (apps) make smart phones a favorite means of communication among users. In general, the available contents on smart phones, apps, and Web, come in two versions: (1) free content that is monetized via advertisements (ads) and (2) paid content that is monetized by user subscription fees. However, the resources, namely, energy, bandwidth, and processing power, on-board are limited, and the existence of ads in Web sites and free apps can significantly increase the usage of these resources. These issues necessitate a good understanding of the mobile advertising eco-system and how such limited resources can be efficiently used. In this paper, we present the results of a novel Web browsing technique that adapts the Web pages delivered to smart phone, based on the smart phone's current battery level and the network type. Web pages are adapted by controlling the amount of ads to be displayed. Validation tests confirm that the system can extend smart phone battery life by up to 30% and save wireless bandwidth up to ~44%.

Cloud Computing with CDroid OS Based on Fujitsu Server for Mobile Technology

The twenty first century made us smart enough, it is now mandatory to save power consumption as much as for the sake of financial, environmental stability. It is equally important for national security and mankind. Today a huge number of populations in the world is worried about Smartphone batteries. They are huge consumer of energy when using several apps. Our system is to develop Cloud Computing with CDroid Operation System that is based on Fujitsu Server for Mobile Technology, which is completely used for saving energy, enhancing battery life and saving processor cycles more than ever of mobile phone, sharing data and faster access. The system shares both the memory of server and phone as well by using offloading mechanism. The mobile cloud computing concept helps several things to conserve power . The newest Fujitsu server provides world's fastest processing. We had described thoroughly the architecture of newly proposed system. This will be one more step towards the quickest changing cell phone technology. The hybrid cloud computing mobile technology for the next generation will be ready for our world within few years.

CDroid: Used in Fujitsu Server for Mobile Cloud

In today’s era, it is important to save energy as much as for the sake of better financial, environmental, national security and above all the mankind. The Smartphone batteries are huge consumer of energy when using several apps. The proposed system is to develop a CDroid operating system in Fujitsu server for mobile cloud computing as saving energy, enhancing battery life and put aside processor cycles of mobile phone, sharing data / information and faster access. Our CDroid system shares both the server and phone memory as well by using offloading mechanism. The Smartphone transmits the data in binary format by using CDroid device stored in it. CDroid device includes application framework to countenance multitudinous applications. The FSK, FDMA and TDM maneuver to locomotion information. CDMA2000 1xEV-DV promote channelized the information that may include both data and voice. The UMTS, WCDMA, HSDPA, EVDO are some protocols to communicate through mobile tower. A mobile tower is made up of microwave, GSM antennas and radio frequency (RF) cables. They intercommunicate with the base station through MTSO. Bandwidth provided for this kind of radio network around 1200 to 1400 MHz. Wireless spectrum is used the RF between 800 MHz to 2.2 GHz bands. The Smartphone uses 0.6 to 3 watts. The CDroid OS inside fujitsu server in cloud is conveyed by ISP’s as per standards. World’s fastest fujitsu server contains CDroid (a server approach) constituents within cloud accommodates the connection handler (secure), optimizer users traffic, caching and pre-fetching; content compression is also done here. The security issues must encompass various apps, anti-phishing, cookie handler, sensitive information blocker, push notification handler and remote wipers. Mobile advertisement blocker, push notification handler protects users’ privacy. Synchronization handler, remote code executor handles mobiles computations and loading the data backup. As the operating system and information are accumulated onto clouds the power necessitate to process consumed into fujitsu server. This preserves Smartphone battery life, besides this mobile cloud server is quickest among others. It provides the winged response to the user. We had described thoroughly the architecture of newly proposed system. This will be next step towards the changing nature of hybrid cloud unsegregated mobile technology for the future.

Energy Efficient Smartphone-Based Activity Recognition Using Fixed-Point Arithmetic

In this paper we propose a novel energy efficient approach for the recog- nition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre- serve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experiments show comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of the proposed method.

Adaptive and Flexible Smartphone Power Modeling

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).

Crowdsourcing urban air temperatures from smartphone battery temperatures

Accurate air temperature observations in urban areas are important for meteorology and energy demand planning. They are indispensable to study the urban heat island effect and the adverse effects of high temperatures on human health. However, the availability of temperature observations in cities is often limited. Here we show that relatively accurate air temperature information for the urban canopy layer can be obtained from an alternative, nowadays omnipresent source: smartphones. In this study, battery temperatures were collected by an Android application for smartphones. A straightforward heat transfer model is employed to estimate daily mean air temperatures from smartphone battery temperatures for eight major cities around the world. The results demonstrate the enormous potential of this crowdsourcing application for real‐time temperature monitoring in densely populated areas.

Predicting smartphone battery usage using cell tower ID monitoring

Personalized power management in mobile devices is a critical issue in handling the diversity of smartphone usage. In particular, usage prediction is important for the efficient use of remaining battery capacity. In this paper, we propose a system that predicts the amount of battery usage required in the future. Our key insight is that a high degree of correlation exists between battery usage and a user’s movements. We design an everyday location monitoring system that only uses cell-tower connections, without additional energy consumption. The technical challenge is eliminating the ping-pong effect in a series of cell-tower transitions to determine the mobility status, especially with limited access to the list of neighboring cell towers. We construct a graph from the sequence of recorded cell towers and recognize the points of interest using a partial clique graph. We use the Markov predictor to estimate the required battery level depending on the user’s movements. We demonstrate the accuracy of battery usage prediction using real traces of participants collected over a period of four weeks. The result shows that the proposed system correctly predicts the battery usage of smartphones, with an 8.1±7.5% margin of error.

Characterizing and modeling the impact of wireless signal strength on smartphone battery drain

Despite the tremendous market penetration of smartphones, their utility has been and will remain severely limited by their battery life. A major source of smartphone battery drain is accessing the Internet over cellular or WiFi connection when running various apps and services. Despite much anecdotal evidence of smartphone users experiencing quicker battery drain in poor signal strength, there has been limited understanding of how often smartphone users experience poor signal strength and the quantitative impact of poor signal strength on the phone battery drain. The answers to such questions are essential for diagnosing and improving cellular network services and smartphone battery life and help to build more accurate online power models for smartphones, which are building blocks for energy profiling and optimization of smartphone apps. In this paper, we conduct the first measurement and modeling study of the impact of wireless signal strength on smartphone energy consumption. Our study makes four contributions. First, through analyzing traces collected on 3785 smartphones for at least one month, we show that poor signal strength of both 3G and WiFi is routinely experienced by smartphone users, both spatially and temporally. Second, we quantify the extra energy consumption on data transfer induced by poor wireless signal strength. Third, we develop a new power model for WiFi and 3G that incorporates the signal strength factor and significantly improves the modeling accuracy over the previous state of the art. Finally, we perform what-if analysis to quantify the potential energy savings from opportunistically delaying network traffic by exploring the dynamics of signal strength experienced by users.

New camera chip captures only what it needs

Bothered by how quickly recording video drains your smartphone battery? There's no app for that-but there may soon be a chip. Researchers at Sony and Stanford have constructed an imaging chip that incorporates a new technology called compressed (or compressive) sensing, to radically reduce battery drain by compressing video frames before they're digitized.

Using Smartphones and Sensor Technologies to Automate Collection of Travel Data

This paper presents a data collection framework and its prototype application for personal activity–travel surveys through the use of smartphone sensors. The core components of the framework run on smartphones backed by cloud-based (online) services for data storage, information dissemination, and decision support. The framework employs machine-learning techniques to infer automatically activity types and travel modes with minimum interruption for the respondents. The three main components of the framework are (a) 24-h location data collection, (b) a dynamic land use database, and (c) a transportation mode identification component. The location logger is based on the smartphone network and can run for 24 h with minimal impact on smartphone battery life. The location logger is applicable equally in places where Global Positioning System signals are and are not available. The land use information is continuously updated from Internet location services such as Foursquare. The transportation mode identification module is able to distinguish six modes with 98.85% accuracy. The prototype application is conducted in the city of Toronto, Ontario, Canada, and the results clearly indicate the viability of this framework.