Recent Advancements in Soft Computing and its Application

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The locator component aggregates the positioning information either from the built-in positioning sensors in the smartphone, a GPS receiver, and a WLAN Wireless Local Area Network or a Bluetooth chip, or any external positioning device, such as also the multi-sensor positioning MSP device developed in this project.

It forwards the positioning information including the location and heading information to the route plan component and the 3D visualization engine to accomplish the navigation functions. Figure 3 shows the overview of the mechanism for delivering the location-based services. The services are classified into two categories: the static services and the dynamic services. The static services include those services that are not changing in time.

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For example, POIs points of interest belong to this category of service. The static services are stored in a database that can be downloaded from the Internet by the users in advance. The users can store the database in the memory card of the phone before running the 3D personal navigation and LBS software.

With this approach, it saves the data transmission fee for the end-users when accessing the LBS. The dynamic services cover those services that change in time.

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For example, a piece of real-time news is one of the typical dynamic LBS. Mechanism for delivering location-based services and information. The LBS client component is implemented so that the handset will pull automatically the news in the background in real time via a widget reader embedded in the LBS client component. Whenever new information is uploaded to the LBS server or to the registered web pages, mobile users will be notified.

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The dynamic LBS information e. The message encoded is then sent to a pseudolite from which the message is broadcast. Having received and decoded the LBS messages transmitted from the pseudolite with a dedicated receiver, for example the MSP device part of the more advanced demonstration scenario of the project, the content of the message is then encoded to a user-defined NMEA National Marine Electronics Association message and transmitted to a mobile phone in the vicinity via a Bluetooth connection as shown in Figure 3.

This solution of LBS data distribution is available only to a very limited number of users with receivers carting a special firmware developed by FGI. Due to the memory limitations of a mobile phone, there are certain requirements for the 3D models applied. In our study, a test scene for model reconstruction is focused on a street in Espoo, Finland, in an ordinary residential area. It consists of a carrying platform, a positioning and navigation system, and a 3D laser scanner system.

A large amount of data is produced from the system, and noise and outlier points are needed to be removed. Valid data is classified into different point groups using an automatic algorithm developed by FGI. These point groups include buildings, trees, roads, and poles. Models are then reconstructed based on these classified point groups. Modeling methods are developed to meet the application requirements of personal navigation: small model size, high accuracy, and good visual appearance.

Small model size is achieved by simplified object geometry and reduced texture resolution. Model accuracy is controled by extracting building outlines from a classified point cloud and overlapping with the final 3D model. To measure data utility loss, the differences between two OD flow matrices derived from the dataset before and after privacy protection were assessed. As the regional unit in Eq 1 , traffic analysis zones TAZs were chosen. TAZs may have different spatial resolutions according to application requirements.

To compare the impacts on data utility of different spatial resolutions, this study used two sets of TAZs, one having TAZs with an average radius of meters, and the other having TAZs with an average radius of meters. The radius of a TAZ is measured by the radius of a circle with the same area. To represent the impact on whole dataset, the time period T is set as the entire time span of the CDR dataset i. For the OD flow matrix before privacy protection, the first step was to apply the concept of transient-origin-destination [ 12 ] to identify movements between base stations.

This method identifies a movement by searching for two consecutive non-identical locations from a trajectory and sets the two locations as origin and destination respectively according to their temporal order. For instance, a person may make a first call at place A in the morning, then make several trips without any calls, and finally make a second call at place B in the evening.

Moreover, in mobile networks, signal transitions between neighboring base stations will produce false short-distance movements [ 14 ]. To reduce these two types of errors in All flows-Raw , travel time of a single trip was used as a constraint to refine the identified movements. This study excluded the movements with time intervals less than 5 minutes or greater than minutes, and named the remaining OD flows as All flows-Constraint.

After privacy protection, suppose there are S aggregated base stations. Finally, Eq 1 is used to calculate the utility loss: 3 where aggregated base station p includes U base stations, aggregated base station q includes V base stations, and OD BS uv is the flux from base station u to base station v : 4 where aggregated base station p with area A p has an overlapping area A p i with TAZ i , and aggregated base station q with area A q has an overlapping area A q j with TAZ j.

Moreover, the Pareto principle also known as the 80—20 rule broadly exists in transportation systems. The Pareto rule suggests that a small proportion of OD pairs contributes most of the flows, and these OD pairs are more important than the remaining ones in a transportation system. Obviously, there exist some major flows that received impact from privacy protection procedures should be particularly examined in the results analysis.

Because the definition of major flows can be various and may affect our analysis results, this study adopted three ways to define major flows. The first one was designed simply according to the Pareto principle. This study names the backbone of networks as Major flows-Backbone. The third one defines major flows as the flows that start from or end with hotspots based on the study [ 35 ].

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This study defined the hotspots as TAZs whose population are higher than a threshold. The population threshold is derived from the Lorenz curve of the population distribution [ 36 ]. This study names the flows selected by the third definition as Major flows-Hotspot. For details of the second and third definitions, interested readers can refer to [ 34 — 36 ]. The three curves representing the top one, two, and three locations show that the k -anonymity value in general decreases with increasing number of top locations, which demonstrates the increased re-identification risk with additional background knowledge about top locations.

Fig 2b shows the k -anonymity values for the top one, two, and three locations unordered, and Fig 2c compares the two curves for the ordered and unordered top two locations.

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Fig 2c also shows the cases using the variable-length top locations ordered and unordered. The two curves are close to the curves of the top two locations. When the k -anonymity value is smaller than 10—20, the curves of the variable-length top locations are located below the curves of the top two locations. As the size of the anonymity set increases, the two curves of the variable-length top locations move higher than the curves of the top two locations, and gradually the two curves become identical.

The k -anonymity values for the 1 st , 5 th , 10 th , and the 50 th percentiles of frequent call users were then compared in the results of this study and those for the entire United States as reported in [ 15 ] Table 2. Shenzhen City had a re-identification risk fairly similar to that of the overall United States in terms of frequent call users. Without results from other regions in the world, the authors currently consider this consistency to be coincidental.

Study [ 15 ] suggests that re-identification risk might be related to the differences between urban and rural lifestyles.

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A more urban lifestyle causes more diversified activities, thus generating smaller anonymity sets. According to this explanation, the degree of urban lifestyle in Shenzhen might be between that of Sacramento and that of Chicago. These results and discussions are based on frequent call users. The significant higher ratio of inactivity mobile users in Shenzhen City could be related with several situations, such as mobile users in Shenzhen City make much fewer calls than in the United States, many Shenzhen mobile users use more than one telephone number or some telephone numbers have been left nearly unused.

One or several of the above possible reasons lead to lower trajectory exposure and lower overall privacy risk for mobile users in Shenzhen City than in the United States. Study [ 16 ] also suggested that their results should generalize to higher population densities based on the following considerations. On the one hand, higher population density tends to increase the anonymity set; on the other hand, more mobile base stations will locate in areas with high population density, thus increasing the spatial precision of trajectories, which should offset the influence of high population density.

However, the results of this research suggest significantly lower privacy risks in Shenzhen City, an area very likely having a much higher population density than the small European country.