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SAPLOC: An Indoor Localization System Based on Smart Access Points

    Indoor localization based on Wi-Fi fingerprints has been an active research topic in the recent years. However, existing approaches do not consider the instability of unreliable Wi-Fi access points (APs), particularly the ones deployed by individual users. This instability impacts the localization accuracy severely, due to the unreliable or even wrong Wi-Fi fingerprints. Ideally, the localization should be done using only the welldeployed APs (e.g., deployed by the facility team of a building). However, in many places the number of these APs is too few to achieve a good localization accuracy. To solve this problem, we leverage emerging smart APs equipped with multi-mode antennas, and build a new indoor localization system called SAPLOC to reduce the number of necessary APs. The key idea is controlling the modes of AP antennas to generate more fingerprints with fewer APs.

                                    

    As shown in Fig. 1, our system consists of three parts: localization server, smart access point (smart AP) and localization client. The localization server is connected to all the smart APsthrough LAN or Internet. A mobile terminal which needs touse our system must have installed the localization client andbe covered by at least one smart AP. When the terminal needsto locate itself, its localization client sends a request messageto the localization server via a smart AP or other wirelesslinks. The server then controls the measurement of RSS inthe client and the switch of antenna modes in the smart APs. The result of measurement is uploaded to the server and the location of the user is finally estimated. Since synchronization schedule is followed, multiple users can locate themselves at the same time, even if the same set of smart APs are occupied.

Synchronous Localization Strategy

    For most of the previous works based on RSS fingerprint, since only one-mode antennas are used in APs, the asynchronous strategies work well because clients can measure the signal strength from these APs at any time. In our system however, every smart AP has two modes, and thus two contiguous measurements may get the results for two different modes. To avoid these phenomena, we must make sure that during the contiguous measurements of a mobile terminal, the modes of the smart APs cannot be changed.

                                

    As shown in Fig. 2, assume that Client A is covered by AP1 when it sends request message to the localization server. The server commands AP1 to change antenna’s mode in Tc seconds. Before the second switch of mode, Client B and C, covered by both AP1 and AP2, also send their requests to the server. By this time both AP1 and AP2 are occupied and their modes are switched at the second Tc. Before the third Tc, Client D, covered by AP2 only, requests to locate itself. When the third Tc comes, Client A has finished measurement, but AP1 is still occupied by Client B and C, and thus its mode is switched for the third time. At the fourth Tc, AP1 is released since Client B and C have also finished measurement. Occupied by Client D, AP2 changes its mode for the third time and finally the server releases it.
    Therefore, following the Synchronous Localization Strategy, our system can provide localization service for more than one user at the same time, and the conflict among localization clients is avoided. Moreover, if we ignore the delay of network transmission, the maximum time costed by the download process will be no longer than 3Tc, which means that the maximum delay in our system is bounded.

Footstep Detection

    In our system, we use the acceleration sensor in smart terminals to detect the number of footsteps a user has taken between two consecutive localization processes. With the information of footsteps we can estimate the distance that the user has walked. Two benefits can be achieved: firstly, taking the distance that the user has moved into consideration can reduce the area of the possible region that the user would currently stay in. Thus we get rid of wrong localization results that are far away from the real position, and the accuracy of localization can be improved. Secondly, since the positions far from the possible region estimated by the footstep detection can be excluded from candidate locations, we can reduce the computation complexity for the Localization Calculator, and the response time is also reduced.

                       

    The comparison for the performance of normal AP and smart AP is shown in Fig. 4. It can be seen that, the average error of SAPLOC is similar to that of the normal AP-based localization system, while only half of the APs are deployed. With 5 smart APs covering the experiment field, the average error can be reduced to 2.90m, while that of the normal-AP case is 3.13m. It can also be seen that the maximum errors of the two systems in different cases are also similar to each other.

 

 


 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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