What makes location based services fail

Map matching algorithms are often employed to improve poor GPS accuracy in urban environments. It might not store and maintain all the information, and very often needs to access and integrate other data sources e. In some LBS applications e. Therefore, there is no need to send the request over the communication network to the service and content provider. However, recently, new built-in car media information systems have adopted new standard e.

Positioning is a key element to support LBS. It involves a variety of location sensor technologies and positioning methods which have been developed for both outdoor and indoor environments. Depending on the development of information technology infrastructures, the mainstream outdoor positioning technologies include global navigation satellite systems GNSS , cellular networks, and wireless networks.

To improve location accuracy in dense urban environments, map matching is often employed. Cellular networks: Nowadays cellular networks are one of the most important communication infrastructures among people and have almost a worldwide coverage. When a mobile phone call is made, the mobile signal is usually linked to the nearest cell tower within its traffic capacity.

The location of the cellular tower can be used to estimate a mobile phone user's location when he or she makes a phone call. The spatial divisions of such cellular networks are divided into cells regions based on the Voronoi diagram in which each cell tower is located its center.

There is a corresponding region consisting of all points closer to that center than to any others. That is, all phone calls within a given Voronoi polygon are closer to the corresponding cell tower than to any other cell towers. Generally, urban core areas have a higher density of mobile cells where the average distance between mobile base stations is approximately one kilometer; the value of average separation depends on the size of the study area Gao et al.

The widely use of Wi-Fi access points for Internet connection in hotels, business buildings, coffee shops, and many other fixed places has made Wi-Fi become an attractive technology for the positioning purpose.

All of those Wi-Fi routers deployed in fixed places repeatedly broadcast wireless signals to the surrounding area. These signals typically travel several hundred meters in all directions such that they can form wireless signal surfaces, and one device could receive distinctive signals at different locations on the surface for localization. The accuracy of localization is then dependent on the separation distance among adjacent Wi-Fi reference points and the transmission range of these reference points Bulusu et al.

Their pros and cons are discussed in terms of coverage, accuracy and reliability. It reports that Assisted-GPS obtains an average median error of 8m outdoors while Wi-Fi positioning only gets 74m of that and cellular positioning has about m median error in average and is least accurate. In general, indoor positioning technologies can be classified into two broad categories: radio-frequency-based RF and non-radio-frequency-based NRF technologies.

Researchers have made advances in indoor positioning using these sensors and technologies. The spatial coverage area and positioning accuracy of those different technologies have been reviewed by Mautz Here, we only briefly discuss the RF technologies that are most popular in the market share and have many challenging issues to investigate.

WLAN: Positioning systems using wireless area local network WLAN infrastructure are considered to be cost effective and practical solutions for indoor location estimation and tracking. The wireless networks are widely implemented in many types of indoor buildings in which wireless access points are usually fixed at certain positions. Those access points allow wireless devices e.

It is also well known that the accuracy of indoor position estimation based on Wi-Fi signal strength is affected by many environmental and behavior factors, such as walls, doors, settings of access points, orientation of human body, etc. Ferris et al. Therefore, a fingerprint positioning approach is often employed, in which the currently measured fingerprints e.

In practical applications, a good approximation of heterogeneous environmental signal surfaces could help to improve the indoor positioning accuracy. Spatial regression, a widely used spatial-analysis method in finding spatial patterns of surfaces, could potentially be a good candidate.

Bluetooth: Bluetooth technology that is designed for low power consumption allows multiple electronic devices to communicate with each other without cables by using the same 2. The distance range within which Bluetooth positioning can work is about 10 meters. In an indoor environment equipped with equal or larger than three Bluetooth low energy BLE beacons, the location of a target mobile device with Bluetooth can be determined using geometric trilateration or fingerprint positioning approaches.

In this way, location-dependent triggers, notifications and tracking activities can be enabled by employing multiple BLE beacons.

There are several popular BLE beacon-positioning industry protocols and technology available online including Apple iBeacon, Google Eddystone, and Qualcomm Gimbal, which guide developers to implement up-to-date indoor positioning and tracking applications.

Radio-frequency Identification RFID : RFID is a general term used for a system that communicates using radio waves between a reader and an electronic tag attached to an object.

Compared with Bluetooth technology, RFID systems is usually comprised of readers and tags that store relatively limited information about the object such as location and attribute information. Currently RFID positioning and tracking systems are widely used for asset tracking, shipments tracking in supply chains, and object positioning in retail places and shopping malls. Because of the sensor diversity and positioning challenges in various indoor environments, there is also an increasing trend towards combining and integrating multiple sensor networks to get better spatial coverage and positional accuracy than using a single data source.

Users are central to LBS. For effectively supporting users, LBS should provide information and services relevant to the location, context, characteristics and needs of a mobile user. The user is a starting point for any LBS application design, and therefore the approach of user-centered design UCD is often adapted. Originally introduced in computer science, UCD tries to optimize the product provided according to what users want and need, rather than forcing them to change their behavior to accommodate the product.

Those not adapted to their users will be dismissed by them. Basic questions about the users in LBS include who are the users; what are their tasks, goals, and thus needs; and what are their preferences and constraints. Compared to users of other GIS systems and applications, the users of LBS are often non-specialist citizens with a wide diversity of interests and needs.

A user model is the collection of personal data associated with a specific user. What data to be considered and modeled depends on the applications. Thoroughly understanding and modeling each user and his or her needs is not an easy task, as users are usually very diverse. Clustering the users in terms of personal profiles, interests, and behaviors is often employed when developing LBS applications.

Based on the classification of users, LBS designers can then determine what kind of information should be provided to them, and in which ways. The information about users can be provided manually by users themselves when first using the application, automatically learned from their past behaviors or interaction with the application, or captured via a hybrid approach. The hybrid approach is a mixture of these methods, and tries to combine the advantages of them.

As the term suggests, location plays an essential role in the domain of LBS. Location models provide means for spatial reasoning typically based on coordinates, e. A location can be represented as geometric coordinates e. Geometric coordinates, as most existing GIS adopt, allow direct distance calculation and topological relationship computation. In contrast to that in geometric coordinates, the distance and topological relations between two symbolic coordinates are not explicitly defined; however, symbolic representations are more in-line with the way how general public use to express locations and spatial features in human dialogues.

Additionally, since no GPS signal is available, the solution also allows for the player to move their avatar via another input such as accelerometers or touch screen. This information may be after that used in the game itself, as can be seen in Figure 1. Additionally, the framework creates a layer above the DirectX mobile framework, allowing the developer to easily create, add, remove, and animate sprites, as well as provide simple collision handling mechanisms.

The game, Geo Wars [ 14 ], is a free-to-play location-based tower-defence game. In it, the player takes the role of a general with the goal of defending their sector a portion of a map from enemy forces, depicted in Figure 2. The player must resist several waves of enemies, either by physically moving around, evading enemy fire, or luring them into friendly crossfire situations, or by building defensive towers, each with its unique strengths and weaknesses.

The enemy attacks by land, air, and sea, using tanks, soldiers, airplanes, and cruisers. While tanks and soldiers can only move around streets or parks, aircrafts can move anywhere in the map. Each unit has its own firepower, primary and secondary objectives some units may prefer targeting specific towers rather than the general , and A. Player uses money to build towers, money that can be gained over time, by destroying enemy units, or by physically moving to the locations of virtual bags of money displayed on screen.

The game loads saved games settings via a web service that accesses a remote database that contains player-related saved games created using the online portal. The testing of this game has been made with both a small group of testers and analysing their game experience and their feedback on the field and through a thread in the xda forum that allowed for a broader group of users to test the game and provide some comments and suggestions.

It was viewed over times, and the Geo Wars game was downloaded and tested by over people. The video depicting the game is also available in that thread. However, since the game does not incorporate any feature that provides us with user statistic, the only feedback gathered was through comments or private messages through xda developers. Most notably was the desire of users to participate in multiplayer games a feature current lacking in the framework , to cache games for later use and to use a custom address for the game to be played at.

Fortunately, these features were easily achieved, as they were already contemplated in the framework. Since the game relies upon Google maps static API, the game is thoroughly available in most locations, a feature that was highly appreciated by the users of the forum.

Remarkably, no tester seemed to be preoccupied with the data connection that was often required to play a noncached game or to include the local weather within the game. An issue that was easily identified was the need to zoom in or zoom out of the map, as in street crowded cities, the game, if not played with a high map zoom option, would be hardly playable. Likewise, if the game was to be played in a rural area, the player would need to zoom out a bit, in order to include more streets for making the game more challenging.

This can be considered as the drawback of playing a game that uses such wide coverage of maps and playable locations. In fact, some users reported that in some locations, the game was either too hard or too easy, demonstrating that the game needed a balancing mechanism.

This allowed for the game to be more playable and the experience more enjoyable. This feature was one that gathered the most consensus. Some of these suggestions and issues were noted only during the testing phase of the Geo Wars game and were yet to be present in the framework. However, whenever possible they were implemented in the framework and used in the game, allowing future games to also profit from these solutions.

Also noteworthy is the fact that not all location-based games may benefit from these solutions or even have these issues. On the other hand, this game itself may also not have made noticeable other problems or solutions that may be used by other location-based games. Due to the unpredictable nature of location-based games, some issues and limitations are difficult to be overcome completely. Instead, most solutions are based on alternatives that allow a game to still be playable rather than a solution to the issue itself such as when the GPS signal is unavailable but the game is still playable by simulating GPS input some other way.

However, such difficulty appeared only regarding hardware limitations. The developed framework incorporated most of these solutions, and thanks to the proof-of-concept game Geo Wars, it was proved to be a viable solution considering the provided positive feedback of its users.

Geo Wars was tested thoroughly by the community at xda-developers. Since the framework proved to be a good solution, extra effort is being put in porting it to 3D with mesh loaders and renderers already implemented as well as including a multiplayer module that it currently does not have as these features were considered by many to be important in such games.

Front Matter Pages i-xxi. Pages Front Matter Pages N1-N1. Design constraints on operational LBS. What makes Location-Based Services fail? The Transition from Internet to Mobile Mapping. Theory and development of research in ubiquitous mapping. Front Matter Pages N2-N2. Location becomes a supporting attribute to ancillary information related to that location such as a street address or other point of interest. Typical applications include automatic location identification ALI for emergency response phone calls made to a dispatch operator.

However, an expanded use of the term is offered to include enterprise computing solutions of Skip to main content Skip to table of contents. This service is more advanced with JavaScript available.