ANALYSIS OF THE REQUIREMENTS

In section 1.2 six requirements have been established the implementation of the P2P Kalman filter has to address or which are necessary to provide mutual filtering. In this section it is examined if these requirements could be fulfilled by the prototype application introduced in 3.4 and the tests 3.5. Each requirement is in the following separately reviewed.

1. Navigation and positioning should also work deep indoors.

The dead reckoning algorithm which is based on step detection, step length estimation, and heading estimation by inertial sensors which is proposed here is not dependent on satellite signal data which is not able to permeate walls and ceilings of a building (compare 2.2.5). Therefore it is possible to obtain a position with this application also in deep indoors. However in the tests made in 3.5.1 the dead reckoning algorithm alone does not provide position accuracy that enables reasonable navigation or location-based services. The additional use of a Kalman filter algorithm improves the position estimation to a certain degree but the error is still too large for navigation. Not until the mutual filtering is allowed the position estimation reaches an accuracy (compare 3.5.3) that enables location-based services and navigation indoors. Still it is obvious that more accurate sensors and additional measurements coming from the gyroscope, the camera or a barometer for 3D positioning would improve the dead reckoning algorithm and therefore also the overall position accuracy for all peers. Nevertheless position estimation based on inertial sensors embedded in a smartphone alone will not be able in the near future to provide position accuracy that satisfies the user’s needs related to pedestrian indoor navigation.

2. The user must not pay additional costs for data transmission of navigation assistance.

The data transmission via WiFi direct as well via Bluetooth is free of costs for all users. No additional costs have to be paid when connecting to other peers to exchange position data for the mutual filtering. Of course the P2P Kalman filter does not exclude the mostly charged connection to a location server to receive AGPS data. As stated in 1.1 and 3.1 the idea is that some peers have access to better position data due to better sensors, better receivers or better data and that the position estimation of these peers is weighted heavier in the mutual filtering algorithm than the position estimation of peers with less advanced devices or no internet connection. But the exchange of position data between the peers is free of costs.

3. The P2PKF should be independent from centralized entities.

As described in 2.4 and especially in 2.4.3 there are several methods to enable indoor positioning based on radio signals but they all have the drawback that centralized entities are necessary to compute the position of a peer and for some approaches the position even has to be stored in this centralized unit (often a server) e.g. RF-ID tags. This poses not only a thread on the user’s privacy but can also be a problem when many users simultaneously want their position to be computed and sent back to them. This problem also occurs for AGPS in MS-assisted mode (compare 2.3.1) since one location server has to provide assistance to an area of about 100 km coverage [15]. Unfortunately it is not possible to obtain information about the network traffic caused only by the request of assistance data but in urban areas it is very likely that there are several requests for navigation assistance. This might not break down the location server but results under certain conditions in long response times for the user.

The fact that the position data or information to compute the current position is transmitted to a centralized unit which might store this and the following position updates along with the name of the user does not inspire trust. As a matter of fact in spring in 2011 there were several articles online but also in newspapers suspecting Apple and Google to not only store the locations of a user in a file and a database respectively on the mobile device but also transmit this data back to servers owned by Google or Apple [96]. It is still not clear what the purpose of the storage of the locations is anyway and if this

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file is actually transmitted personalized to servers. Nevertheless this illustrates that centralized units might indeed threaten user’s privacy. Of course this is also an issue for the P2PKF since position data is exchanged between users that do not know each other. However the P2PKF proposes an autonomous way to estimate the position. Even the exchange of position data and its mutual filtering is handled in a decentralized way.

4. The mobile device is equipped with inertial sensors to estimate step length, step and heading and a satellite navigation receiver.

This is a basic requirement of the P2P Kalman filter. The estimation of the user position is dependent on the measurements of integrated inertial sensors in the smartphone. In contrast to inertial sensors which are used in standard inertial navigation the MEMS integrated in the smartphone provide very noisy measurements and therefore the estimated position is very noisy as well. The sensors can therefore not be regarded as reliable especially when considering the results in the tests described in 3.5.1. Nevertheless the here introduced approach provides a position estimate based on the MEMS integrated in the mobile phone. No additional equipment is necessary only the units which are standard in today’s mobile devices (compare 2.5.2.2) are used for the position estimation and the exchange of data. It is expected that the measurement accuracy of MEMS still can be improved. However the reason for the often noisy measurements is not alone based on the quality of the MEMS but also on the high density of electromagnetic sources in the smartphone itself. Due to the strong convergence of technology in one device even more measurement noise can be caused although the MEMS are improving. As stated before the positioning based on dead reckoning can further be enhanced by integrating measurements from other sensors (e.g. barometer) and hardware (e.g. camera). Still the idea is to use the smartphone as it is installing only software in form of an application on it.

5. The mobile device must be able to communicate with other mobile devices within an ad-hoc network.

This is the core of the P2P Kalman filter. Since inertial navigation based on the built-in sensors is not able to provide a position estimate accurate enough for navigation or location-based services as already explained in detail in the paragraphs before the mutual filtering of the position data in a Kalman filter algorithm is necessary to improve the position accuracy. Therefore interfaces to provide access to radio ad-hoc networks like WiFi direct, ZigBee, and Bluetooth are necessary. The ZigBee standard was considered for the P2P Kalman Filter but it is still unfamiliar to a large extent and therefore not integrated into current smartphones although it would be applicable to use it for data exchange of the peers especially regarding its network structure, see 3.2.3.2.

Bluetooth was implemented first to enable the communication between the peers but showed some serious deficits related to its security policy. The user has to allow each incoming connection request and the device is only visible for a certain time. As described in 3.5.3 this is not acceptable for this application although Bluetooth provides a beneficial cell size, a well-defined interface and a large popularity.

WiFi direct was developed and published only recently therefore this standard is not very common yet. Nevertheless it is a suitable candidate for this application since the security mechanisms are not as strict as for Bluetooth. One draw is the relatively large coverage area and the fact that it is only available for mobile phones which are updated to at least Android 4. However modules which provide Bluetooth communication are common in each smartphone and also WLAN functionality can be found in almost all smartphones today and it is expected also for future generations of devices. More complicated is the fact that, due to the current limitations of WiFi direct, all peers have to reside within one group. The maintenance of this permanent connection uses too much processing power and disables the processing of other tasks of the P2PKF let alone that other applications can run simultaneously on this mobile phone. With the upcoming of mobile devices with more efficient processing power and potential changes of the WiFi direct implementation the P2PKF could be processed as planned on smartphones.

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Nevertheless it is necessary to control the amount of concurrent connections to enable the use of the smartphone for other tasks than positioning.

6. The P2PKF should enable a continuous positioning when moving from outside into indoors and vice versa. The survey of indoor area should not be necessary to enable positioning.

As described in 3.5.2 the switch between outdoor and indoor positioning can be performed by the application automatically. This behavior is based on the reception of NMEA strings in the mobile phone. As soon as within a certain time interval no NMEA data is received anymore the devices switches to dead reckoning based on the last position estimated with GPS data (see 3.5.2). For the user this does not make a difference. The application continues to compute a position and provides it to the user. Also when leaving an indoor area, the application is able to change from inertial navigation back to navigation via satellite signals. In 2.5.3 it was mentioned that the application currently does not support the filtering of the GPS position with the position estimated by inertial sensors. This is an issue which should be addressed in future work.

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