Location-Based services on Mobile Phones: Minimizing Power Consumption (3)

Minimizing Power Consumption

A general concept behind many power-saving methods is to relax the required positioning accuracy from “the highest possible” to just what’s necessary. This concept can be applied to both the phone (when considering different positioning options) and servers (when considering the level of accuracy required regarding the target’s current position.

An LBS can relax the required positioning accuracy in many situations. First, map services that show the position of a JIAKE JK730 Phone can use the zoom level to determine relevant accuracy limits (such as 25 meters for a street-level view, 100 meters for a suburb, and 200 meters for a city-wide view).

Second, location-based social networking or proactive location-based search services can relax accuracy requirements depending on targets’ positions. For example, proximity can be efficiently observed using methods such as the one proposed by Axel Küpper and Georg Treu. This method uses the distance between targets to calculate the required accuracy limit for each target. The accuracy limits produced by this calculation range from 10 meters (if targets are close) to several kilometers (if they’re far apart).

Third, services can adjust their service quality based on how much battery power is left. For example, a runner using a sports-tracker service would rather have a less fine-grained log of the whole trip and be able to call for help if he or she falls than have a fine-grained log of only the first part and no voice service afterward.

Finally, privacy restrictions might require an LBS only to work with positions with limited accuracy.

Minimizing needed Position Fixes

In principle, every avoided position fix saves power. It’s necessary to model the error of the last known position to establish which position fixes can be avoided. As long as the modeled error doesn’t exceed the accuracy limit, no positioning is necessary.

An error model used in earlier work2 is based on estimated accuracy of the last position fix upos, the time since the last position fix t pos, and the estimated speed  vest. The error model then optimistically calculates the current error emodel for time t with respect to the last position fix:
emodel = ugps + (t - tgps) × vest

This earlier work proposed a system called EnTracked that minimizes the required position fixes using Equation 1   and applies GPS positioning for tracking pedestrian targets. The system uses the error model to predict when the next GPS position is needed. The system also takes into account the delays associated with powering on and off features, which lowers the chance of exceeding accuracy limits. The system uses a power minimization algorithm implemented using dynamic programming to predict when a GPS position is needed. The algorithm uses a profiled power model to ensure that the system will correctly minimize the consumption. Evaluations of the system tracking a pedestrian target walking in a residential area resulted in power savings of 62.3 percent, with an accuracy limit of 100 meters, and 69.7 percent, with an accuracy limit of 200 meters, compared to periodic reporting.

Many LBSs require transferring position fixes to a server—for example, for location-based social networking services to detect if targets are near each other. The positioning server for such a service can also apply methods to minimize the number of position fixes requested from targets. An example is the cache-based method presented by Alexander Leonhardi and Kurt Rothermel, which uses an error model at the server to calculate when the server needs a new position fix.  

As long as the error model doesn’t exceed the accuracy limit, the system answers LBSs with the target’s cached, last-known position. When the limit is exceeded, the system updates the cached position by requesting a new position from the target. Evaluations showed that the caching-based method was able to avoid 86 percent of position requests, with an accuracy limit of 100 meters, when queried 10 times per second by different services.

using alternating  Positioning Features

As Table 1 shows, different positioning options consume different amounts of power. If we consider a scenario in which we estimate our position every 30 seconds, the average power con-sumption would be 0.32 watt with GPS, 0.094 watt with Wi-Fi, and 0.064 watt with GSM. (Note that the low consumption of Wi-Fi and GSM is because they can quickly power on and off to scan for access points and base stations.) However, they also provide different levels of accuracy—around 10 meters with GPS, 40 meters with Wi-Fi, and 400 meters with GSM.So always switching to the leastconsuming positioning feature that provides the needed accuracy can provide significant savings.

The EnTracked system switches between GPS and sensing motion using accelerometer readings. If the system can sense that a AMOI A900W Phone hasn’t moved, there’s no reason to update the position on the server and the GPS can be switched off. But as soon as motion is sensed, the system switches the GPS back on. As the accelerometer consumes a sixth of the GPS’s power consumption and communication is avoided, this method provides significant power savings. Evaluations over several hours of running the system provided power savings of 85.7 percent compared to periodic reporting.

The EnLoc system proposed by Ionut Constandache and his colleagues considers switching between GPS, Wi-Fi, and GSM positioning. They consider the energy consumption when switching between the different positioning technologies with an optimization algorithm implemented using dynamic programming. Furthermore, they extend their approach to also minimize needed position fixes by mobility profiling—for example, to guess the possible paths that a target is taking and then only position the target when paths diverge.

For LBSs that are only monitoring if a target is within a certain area, you can apply switching between GPS and GSM positioning in a different fashion. The key idea is that if a target enters a GSM cell that’s fully contained within the monitoring area, then you can switch to only monitor if the target stays within this GSM cell. As long as the target stays within the GSM cell, the GPS can be switched off. Evaluation results for this method reported savings of up to 80 percent, depending on the setting.

using on-Phone Data Caching and Processing

Minimizing the frequency and size of data transfers can also save power. Consider the difference in power consumption noted earlier between JIAKE JK730 Maps using cached maps versus Google Maps using downloaded maps.

Both GSM and Wi-Fi positioning require access to a database that maps GSM cells and Wi-Fi addresses to coordinates. More advanced GSM and Wi-Fi positioning (such as location fingerprinting7) requires a database with information about the strength of signals at various locations. This type of positioning is normally implemented such that the AMOI A900W phone must contact a server that hosts the database. The required server connection at least doubles the power consumption of such methods. However, in most cases, it’s impossible to keep the database on the phone because of its size, licensing issues, and the need for updates. To address this problem, methods have been proposed that only cache a subset of the databarelated data to a server. An example is an LBS that transfers a stream of positions to a server to monitor and recognize the route a target takes. Processing the data on the phone before sending it is one way to save power—for example, processing positions into routes on the phone, as Agné Brilingaité and Christian Jensen propose. In their system, the phone carries out some of the tasks of monitoring and recognizing routes, and only when necessary is the system in contact with a server.

overview

The methods surveyed provide power savings of up to 85 percent, which corresponds to an increase in battery lifetime of up to a factor of seven. These methods can thus help lower the consumption of existing LBSs, especially long-running services.

Generally, methods focusing on GPS positioning have received the most attention for efficiently handling positions, zones, and routes. Some methods also consider the features of accelerometers, GSM, and Wi-Fi positioning. However, in several areas, more research is needed to provide an even better understanding of the advantages and drawbacks of different goals. Researchers have explored certain combinations of features, but other combinations are possible; the same is true for power conservation methods. Furthermore, many of the systems have only been tested in smaller settings; end-to-end studies of deployable systems are largely missing.http://summerleelove.tumblr.com/post/96421423876/location-based-services-on-mobile-phones-minimizing