Energy-Efficient Localization via Personal Mobility Profiling

Constandache, Ionut; Gaonkar, Shravan; Sayler, Matt; Choudhury, Romit Roy; Cox, Landon P.

doi:10.1007/978-3-642-12607-9_14

Cited by 7 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For sampling geometric user positions, an online heuristic has been proposed which predicts the habitual movement of users to estimate the most efficient times at which a new position should be obtained [3]. In contrast, Wang et al [4] focus on a discrete state-based model of user context and derive a Markov-optimal sensing policy using a stochastic optimization framework based on a Constrained Markov Decision Process (CMDP).…”

Section: Related Workmentioning

confidence: 98%

“…Therefore, new context-aware applications will only be tolerated by end users if they do not impede the everyday usage of mobile phones. In order to address this challenge, a more energy-aware design of mobile applications has been advocated [3], [4]. The rationale of this design principle is that applications should be controlled in terms of their energy consumption and may consume only a defined limited energy budget.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Predictive Protocol for Mobile Context Updates with Hard Energy Constraints

Föll

Berg

Herrmann

et al. 2012

2012 IEEE 13th International Conference on Mobile Data Management

View full text Add to dashboard Cite

As mobile devices have become powerful sensor platforms, new applications have emerged which continuously stream mobile user context (location, activities, etc.). However, energy is a limited resource on battery-equipped mobile devices. Especially frequent transmissions of context updates over energyexpensive wireless channels drain the battery of mobile devices in an uncontrolled manner. It is a fundamental algorithmic challenge to design protocols such that users can control the energy consumption on mobile devices while, at the same time, optimizing the quality of mobile applications.To address this trade-off in the area of context update protocols, we propose a novel protocol that maximizes the context accuracy perceived by a remote consumer while guaranteeing that the consumed energy stays under a given limit. Our update protocol exploits predictions about a user's future behaviour to give priority to the most effective context updates. In our evaluation, we apply our predictive update protocol to a realworld trace of user context and show that the context accuracy is significantly increased compared to an update protocol which operates without predictions under the same energy budget.

show abstract

Section: Related Workmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A Predictive Protocol for Mobile Context Updates with Hard Energy Constraints

Föll

Berg

Herrmann

et al. 2012

2012 IEEE 13th International Conference on Mobile Data Management

View full text Add to dashboard Cite

show abstract

“…Assuming that the GPS sensor is one of the most energyconsuming sensors, researchers investigate the possibilities to automatically change the source of position [12]. This approach leads to decrease the usage of energy-consuming sensors, such as GPS, and utilizes other sensors, such as WiFi or GPRS.…”

Section: Related Workmentioning

confidence: 99%

“…Another approach is about understanding the users' mobility patterns to predict their trajectories. This leads to a reduced need for sampling the location continuously [25,12]. However, there is a lack of research into the strategy of minimizing the communication.…”

Section: Introductionmentioning

confidence: 97%

Energy-efficient Position Tracking via Trajectory Modeling

Nodari

Nurminen

Siekkinen

2015

Proceedings of the 10th International Workshop on Mobility in the Evolving Internet Architecture

View full text Add to dashboard Cite

In recent years, the market of location-aware applications has become increasingly larger. However, one of the main problems is still partially unsolved: battery consumption. The low operating time of the batteries hampers the opportunities for collecting users' location data in order to provide a variety of services, ranging from events suggestion to sport tracking. This article proposes a way to diminish the battery consumption by reducing the communication between the clients and the server. The approach is to model the trajectory of the users, so that the clients communicate a new position only when necessary: namely, when the predicted position excessively deviates from the actual one. In order to demonstrate the idea, a prototype has been implemented. This paper evaluates the performance of the model-based technique and it compares the proposed approach with the approach commonly used for location tracking in mobile devices.

show abstract

“…However, the rate of battery power consumption also depends on the type of positioning system. It has been shown by Constandache et al [4]that, in general, GPS positioning drains the battery of the mobile more rapidly compared to GSM or WLAN positioning.…”

Section: System Analysis: Major Factors Related To Handover Among Posmentioning

confidence: 99%

Context aware handover algorithms for mobile positioning systems

Khan

Silvadorai²,

Tan

et al. 2014

csci

View full text Add to dashboard Cite

This work proposes context aware handover algorithms for mobile positioning systems. The algorithms perform handover among positioning systems based on important contextual factors related to position determination with efficient use of battery. The proposed solution is implemented in the form of an Android application named Locate@nav6. The performance of the proposed solution was tested in selected experimental areas. The handover performance was compared with other existing location applications. The proposed solution performed correct handover among positioning systems in 95 percent of cases studied while two other applications performed correct handover in only 50 percent of cases studied. Battery usage of the proposed solution is less than one third of the battery usage of two other applications. The analysis of the positioning error of the applications demonstrated that, the proposed solution is able to reduce positioning error indirectly by handing over the task of positioning to an appropriate positioning system. This kept the average error of positioning below 42.1 meters for Locate@nav6 while the average error for two other applications namely Google Latitude and Malaysia maps was between 92.7 and 171.13 meters.Keywords handover among mobile positioning systems, power efficient mobile positioning, switching between indoor and outdoor positioning systemsComputer Science • 15 (2) 2014 http://dx

show abstract

Energy-Efficient Localization via Personal Mobility Profiling

Cited by 7 publications

References 16 publications

A Predictive Protocol for Mobile Context Updates with Hard Energy Constraints

A Predictive Protocol for Mobile Context Updates with Hard Energy Constraints

Energy-efficient Position Tracking via Trajectory Modeling

Context aware handover algorithms for mobile positioning systems

Contact Info

Product

Resources

About