An Indoor Localization Algorithm for Lighting Control ... - CiteSeerX

An Indoor Localization Algorithm for Lighting Control using RFID Zi-Ning Zhen, Qing-Shan Jia, Member, IEEE, Chen Song, and Xiaohong Guan, Fellow, IEEE

Abstract— Occupant’s identity and location are important information for lighting control in order to reduce the energy consumption while increasing livelihood. While active RFID system provides occupant’s identity, it is nontrivial to localize the occupant’s location in an indoor environment due to the multipath effect, the changing environment, and the unreliable communication link. In this paper, we implement a system with multiple active RFID readers, and develop a localization algorithm based on support vector machine (SVM). The algorithm uses round-robin comparison to localize the occupant to one of the multiple regions in a floor. The geometric relationship among the rooms and the historical localization data are used to further improve the localization accuracy. Numerical results demonstrate a high localization accuracy of this algorithm. We hope this work sheds insight on lighting control for energy saving and an increased livelihood. Index Terms— Regional localization, RFID, lighting control, building systems.

I. I NTRODUCTION The global energy crisis is one of the most serious problems confronting the human race now as well as in the near future. The energy consumption related to buildings has become a large part of the total energy consumption over the world. According to a previous study, two-thirds of the electricity generated in the United States is used for commercial buildings, and one third of energy consumption in China occurs in buildings. The situation is not significantly different in most other developed and developing countries. Lighting systems consume about 20% of the electricity inside a building. Lights are mostly used to serve the human needs and to make people feel comfortable. Occupant’s identity and location are thus important for light control in order to reduce the energy consumption while increasing livelihood. While active RFID system provides occupant’s identity, it This work was supported by the National Natural Science Foundation of China under NSFC Grant (Nos. 60704008 and 60736027), the National New Faculty Funding for Universities with Doctoral Program (20070003110), the Programme of Introducing Talents of Discipline to Universities (the National 111 International Collaboration Project) (No. B06002), the Tsinghua-UTC Research Institute for Integrated Building Energy, Safety and Control Systems, and the United Technologies Research Center. Zi-Ning Zhen was with Department of Automation, Tsinghua University, Beijing 100084, P.R. China (e-mail: [email protected]). Qing-Shan Jia and Xiaohong Guan are with the Center for Intelligent and Networked Systems (CFINS), Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, P.R. China (phone: +86-10-62773006; fax: +8610-62796115; e-mail: [email protected], [email protected]). Chen Song is with the United Technologies Research Center, China and the Tsinghua-UTC Research Institute for Integrated Building Energy, Safety and Control Systems, Beijing 100084, P.R. China (phone: +86-10-62772183; fax: +86-10-62770554; e-mail: [email protected]). Xiaohong Guan is also with SKLMS Lab and System Engineering Institute, Xian Jiaotong University, Xi’an, China 710049. Qing-Shan Jia is the corresponding author.

is nontrivial to localize the occupant’s location in an indoor environment due to the multipath effect, the changing environment, and the unreliable communication link (detailed analysis is shown later). So indoor localization based on active RFID system is important for better lighting control in order to reduce the energy consumption and increase the livelihood. In this paper, we study how to localize an occupant who wears a tag in an indoor environment. There is an increasing trend to apply RFID in our daily life such as supply chain, manufacturing system, inventory control, etc. A RFID system consists of a small number of readers and usually a large number of tags (say hundreds). Due to the cheap production cost of the tags, RFID has economic advantage comparing to other systems such as wireless sensor networks that can provide identity of the objects, and can provide tags’ identities comparing with infrared systems. But in many cases, RFID is usually used for counting rather than localization based on the Received Signal Strength Indication (RSSI). Due to this, localization based on active RFID system in an indoor environment has the following challenges. First, the multipath effect. Due to the reflection, transmission, and diffraction of the radio signal, when an active tag sends radio signal to the reader, the RSSI received by the reader is not monotonically decreasing when the tag-reader distance increases. This is known as the multipath effect in wireless communication. This also leads to the fact that a reader may receive the same RSSI when tags are placed at different positions. Second, the changing environment. The RSSI depends on the environment such as the position of the desks, the close/open of the door and window, and even the other occupants’ behavior. Since these factors may change, for example the occupants’ behavior changes all the time, RSSI changes even when both the tag and the reader are fixed. Third, the unreliable communication link. The indoor communication is unreliable. On the one hand, a changing environment could cause the reader to receive no signal from the tag in some time. On the other hand, when tags are placed at different positions, there could be positions that the reader, which is fixed, receives no signal. To handle the above three challenges, we develop a localization algorithm based on supporting vector machine (SVM). First, to handle the multipath effect, we use multiple readers. When a tag is placed at different positions, the RSSI’s received by one reader could be the same. But the RSSI vectors that represent the RSSI’s received by all the readers are usually different. Second, to handle the changing environment, we relax the requirement on the localization accuracy. Instead of pursuing point-to-point localization, we

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are satisfied by regional localization, in which a tag is localized to one of the multiple regions in a floor. Since the environment usually is different when the localization algorithm is trained and when the algorithm is used, we need an algorithm that has good generalizability in new environment. So we use SVM, which is known for simple and good generalizability. Multiple classifiers are used and a round-robin comparison mechanism is used to classify among multiple regions. Third, to handle the unreliable communication link, on the one hand the multiple tag-reader system reduces the chances for all readers to receive no signal from the tag. On the other hand, we utilize the historical data in the rare cases when no signal is received. Fourth, geometric relationship among the rooms is used to further improve the accuracy of the localization algorithm. Tags send signals to readers regularly. We make the time difference between two neighboring sending reasonably small (say 1 second) so that the occupant (tag) only moves within the region in the last moment and the neighboring regions. This reduces the number of classifiers to consider in the localization and also reduces the number of comparisons thus needed. The major contribution of this paper is to develop a SVMbased localization algorithm that uses multiple readers, uses round-robin comparison to do regional localization, and utilizes the geometric constraints and historical data to improve the localization accuracy. This system has been implemented and tested in a floor of size 382m2 . Numerical results show that it took only 20 minutes to train the parameters, and achieve high localization accuracy, small delay, and relatively robust to the changing indoor environment. The rest of this paper is organized as follows. In Section II we briefly review the state of the art in indoor localization methods. In Section III, we introduce the regional localization algorithm. Numerical results are presented in Section IV. We briefly conclude in Section V. II. L ITERATURE R EVIEW In this section we briefly review the existing techniques for occupancy localization in an indoor environment. We can divide the exiting techniques into two types according to whether the Radio Frequency (RF) signal is used. The non-RF techniques usually suffer from lack of penetrability. While the RF techniques usually suffer from the nonmonotonicity in RSSI when the transmitter-receiver distance increases, which is caused by the multipath effect. We review the two types of techniques respectively as follows. A. Non-RF Techniques Active Badge Location System [1] is an indoor localization system based on infrared, which was first developed in early 1990s. Occupant wears an infrared transmitter, which is called the Active Badge. The Active Badge periodically emits infrared. Multiple infrared receivers are deployed inside the room. Based on which receivers receive the infrared emitted by the Active Badge, the regional location of the occupant

is determined. Since it is hard for infrared to penetrate the clothes, the Active Badge must be worn outside the clothes. Cricket Location-Support System [2] is an indoor localization system using both infrared and ultrasound. Using the time difference of arrival (TDOA) between the infrared and the ultrasound, the distance between the transmitter and the receiver can be determined. Following this way, the distance between a transmitter and multiple receivers (or in the other way the distance between a receiver and multiple transmitters) can be determined. Based on the positions of the multiple receivers, the location of the transmitter can be determined. Due to the lack penetrability of the infrared and the ultrasound, the transmitter/receiver must be worn outside the clothes. B. RF Techniques RADAR [3] is an indoor occupant localization system based on 802.11 Wi-Fi technology. As Wi-Fi is a well developed technology, the RADAR system can be set up easily. It records the radio signal strength indicator (RSSI) the RF receivers get at various locations.. Then, in the runtime localization, the system can find the most possible location by comparing the RSSI with the recorded RSSI. However, if the environment changes a lot, the system should be trained again. SpotON [4] is an indoor occupanct localization system based on Ad-Hoc wireless sensor technology. The unique feature of the system is that it can do a 3D localization. In this system, sensors can be placed randomly and widely. Through the communication of the sensors without a central controller, the location of the transceiver can be calculated by an aggregation algorithm. LANDMARC [5] is an indoor occupant localization system based on active RFID. Since the tags are cheap, a large amount of reference tags are placed inside the room to handle the changing environment. Then, the RSSI from the reference tags that is received by the receiver is used as the runtime training data. As long as the density of the reference tags is high enough, the LANDMARC system can achieve high localization accuracy. MERIT [6] is an indoor occupant localization algorithm based on MICA2 sensor network. The system reduces the multipath effect through placing more readers, which is called Spatial Diversity. Also it can receive RF directionally by setting up metal boards behind readers, which is called RF Reflector. It was reported by numerical results that when four readers that are equipped with RF Reflectors are placed at the four corners of a room, the regional localization is correct with ratio of 98.9%. Besides the systems reviewed previously, the localization technology based on Wi-Fi and fingerprint, which is developed by Cisco Corporation [7], and the UWB-based localization technology developed by Ubisense Corporation [8] also show a powerful performance. We develop an indoor occupant localization method based on active RFID. Detailed introduction of this method is presented in the next section.


III. R EGIONAL L OCALIZATION BASED

ON

ACTIVE RFID

As shown in Fig. 1, we need to locate the occupant in a floor with 4 rooms and a total area of 382m2 . The 4 rooms are separated by glass walls, which can reflect the RF signals and allow the RF signals to pass with little attenuation. As aforementioned the multipath effect, the changing environment, and the unreliable communication link make the localization in this indoor environment nontrivial. In this section, we show how to handle the three challenges in details. First, to handle the multipath effect we use multiple readers. Due to the multipath effect, the RSSI does not drop monotonically when the tag-reader distance increases. In other words, when a tag is placed at different distances away from the reader, the RSSIs received by the reader could be the same. However, when multiple readers are used, the RF signal sent out by a tag can be received by multiple readers, which then constitute a RSSI vector. When a tag is placed at different positions, the RSSI vector is usually different. In Fig. 1, we use 7 readers, which are represented by white nodes from R1 to R7. Second, to handle the changing environment we first relax to a regional localization instead of pursuing point-to-point localization. Each of the 4 rooms is divided into 3 regions. Then there are 12 regions in total, which are labeled from A to L in Fig. 1. Then based on SVM we develop a round-robin comparison mechanism to localize a tag to one of the 12 regions. Details of this roundrobin comparison mechanism are presented in Section III-A. Third, to handle the unreliable communication link, historical data is used when no signal is received. Fourth, the geometric relationship among the rooms is used to further improve the accuracy of the localization algorithm. More details on this are presented in Section III-B.

easy calculation. So for each two of the twelve regions we construct a SVM classifier. There are 12 2 = 66 such classifiers. The issue then is how to combine these classifiers into a 12-class classifier. We develop the round-robin comparison mechanism to address this issue. Let us use a 3-class classification example to explain the basic idea of the round-robin comparison mechanism. As shown in Fig. 2, there are samples from three classes A, B, and C. Each of the two classes are linear separable, meaning the samples from two classes can be separated by a hyper plane. The three lines in Fig. 2(a) represent the three hyper planes that separate A-B, B-C, and A-C, respectively. For samples in A, classifier A-B and A-C output the same classification result. Similarly for samples in B and C, two of the three classifiers agree in the classification result. However, if a sample drops in the grey triangular area in Fig. 2(a), the three classifiers disagree with each other. A faithful decision in this case should be tie. A tie can also occur when two classes cannot be perfectly separated. Note that the multipath effect makes it possible for the readers to receive the same RSSI vector when a tag is placed in different regions. So we generalize the two-class classifier and allow to output tie besides the labels of the two classes. Then each line in Fig. 2(a) is replaced by two parallel lines in Fig. 2(b). For samples drawn from the area between the lines, the classifier outputs a tie. For other samples, the classifier outputs an unambiguous label. After combining the outputs of the three classifiers, we can determine the label of a sample if the sample is drawn from the white area in Fig. 2(b), and output a tie for samples from the grey area.

(a)

Fig. 1. The experimental environment. Each of the 4 room in this floor id divided into 3 regions. Each region is shown in grey. The white points represent the positions of the readers.

A. Round-Robin Comparison To locate the tag into one of the twelve regions, we need to classify the 7-dimensional RSSI vector received by the seven readers into one of the twelve classes, which is a multi-class classification problem. We solve this multi-class classification problem through multiple two-class classifiers. The supporting vector machine (SVM) is a useful classifier for two-class classifiers due to the good generaliability and

(b)

Fig. 2. Explanation of the Multi-class classifier used in the Round-Robin Comparison. When the samples fall in the grey area, a tie occurs. (a) Each line represents a two-class classifier. (b) Each two parallel lines represent a two-class classifier that allows to output ”tie” when the sample falls in between the lines.

Now we mathematically formulate the above idea. Let gij (x) be the SVM classifier [9] between region i and j, where x is a 7-dimensional RSSI vector, and gij (x) takes real value, and satisfies gij (x) = −gji (x) and gii (x) = 0. Let G(x) = [gij (x)]12×12 be the matrix of gij . When a RSSI vector x is received by the readers, the 12 regions compete with each other through the 66 classifiers and collect ”points” 3, 1, or 0, i.e., we define ⎧ ⎨ 3, gij (x) ≥ τ, 1, −τ < gij (x) < τ, (1) cij (x) = ⎩ 0, gij (x) ≤ −τ,


where τ ≥ 0 is a constant. We set τ = 0.3 based on experimental data. Of course the values 0, 1, and 3 can also be changed. We use the above values in the experiments. This above comparison is like the round-robin comparison used in sports games. So we call the above mechanism roundrobin comparison mechanism. Then C(x) = [cij (x)]12×12 is the ”credit” matrix, containing how many points each region obtains during the round-robin comparison. And the rowsum of C(x) is the total points collected by a region. One example of G(x) and C(x) is shown in Table I. A naive way to locate the region then is to output the region with the largest row-sum in C(x). However, this drops all the information contained in the row-sum of other regions, and makes the localization result sensitive to x. For example, when the tag does not move but some people enters the room (so the environment changes and this changes the RSSI field inside the room), the above naive method may locate the tag to other regions, sometimes even to region that is not neighbor of the true region. To increase the robustness of the localization result w.r.t. the changing environment, we first sort the regions according to the row-sum of C(x, t) at time t. If region i ranks r(i, t) ∈ {1, 2, . . . 12}, we define quantification function q(i, t) as follows 1 , t > 1 (2) q(i, t) = αq(i, t − 1) + (1 − α) r(i, t) q(i, 0) = 0, where α ≥ 0 is constant. We use α = 0.6 in the experiments. Function q(i, t) utilizes the historical information. We then select the region with the largest q(t) as the location of the tag at time t, i.e., R(t) = arg min {q(i, t)} , i∈Ω

(3)

where Ω is the set of indices of all the regions, e.g., Ω = {A, B, . . . L} in our experimental scene. B. Incorporating Geometric Information We can incorporate the geometric relationship among the rooms to further improve the localization accuracy. Note that the rooms are separated by glass walls, which allow the RF signal to pass with little attenuation. For example consider two positions. One is for the occupant to be in region C and standing beside the wall that separates region C and D. The other is for the occupant to be in region D and standing beside the wall that separates region C and D. The two RSSI vectors that the readers receive are very close. And the localization result could jump between region C and D, which is not reasonable since occupant usually does not move that fast. Suppose the time difference between two consecutive transmissions of the RF signal of the tag is very small so that the occupant moves within the neighboring regions. We can then restrict the localization result within the neighboring regions, i.e., to modify Eq. (3) to the following R(t) = arg

max

i∈N (R(t−1))

{q(i, t)} ,

(4)

where N (R(t − 1)) is the set of indices of the regions in the neighbor of R(t − 1), including R(t − 1).

In the experiments, the tags send RF signal every second. In normal conditions occupant walks no faster than 5 meters per second. The area of each of the 12 regions is about 5by-6 square meters. So the above assumption that occupants move within neighboring regions is reasonable in normal conditions. In emergent conditions or some other occasional conditions, the occupant moves fast so that moves outside the neighboring region. Then the occupant is localized to the neighboring region which is usually the closest to the true region. Then usually in the next time moment, the occupant is localized to the true region, which by then is in the neighboring regions. The above method usually works. But, if the occupant keeps on moving fast for a while, the localization result could get stuck at a region. We show one such example in Fig. 3. Suppose the occupant moves fast from region B through L, K, E, to D. By the time s/he stops, the localization result could still be region L. Since D is not in the neighbor of L, the algorithm will not localize the occupant to D. There are three regions in the neighbor of L, namely B, L, and K. However, L is the closest to D among the three. So the localization result will get stuck at L. To deal with this issue, we compare maxi∈N (R(t−1)) q(i, t) and maxi∈Ω\N (R(t−1))q(i,t) . If the latter one is greater than the former one and this relationship keeps for a while (say n seconds), we shift the localization result to that region. The value of n is a tradeoff between the robustness and the sensitivity. A smaller value of n means the algorithm is more sensitive (meaning can track the occupant even if s/he moves fast) but less robust (meaning the change in the environment could change the localization result). On the contrary, a larger value of n means the algorithm is more robust but less sensitive. The value of n is usually determined by experiments.

Fig. 3. An example for the localization algorithm in Eq. (4) to get stuck. Suppose the occupant takes a tag, runs fast from region B through L, K, E to D, and then stays at D. The localization result may get stuck at region L.

We summarize the regional localization algorithm as follows. 1) At time t, the tag sends RF signals. The readers receive a RSSI vector x(t). 2) Calculate the matrix G(x, t). 3) Use the round-robin comparison to obtain the credit matrix C(x, t). 4) Obtain the rank of each region r(i, t) according to the row-sum of C(x, t). 5) Update the value of q(i, t) for regions i = 1, 2, . . . 12,


TABLE I A N EXAMPLE OF G(x) AND THE ROUND - ROBIN COMPARISON .

A B C D E F G H I J K L Win Tie Loss Point Rank

A N/A 2.754 1.6214 1.6679 0.9212 0.6156 0.2744 0.3375 0.2456 0.4982 0.896 1.6018 0 2 9 2 12

B -2.754 N/A -0.3157 2.0533 0.9074 0.5227 -0.0256 0.1963 -0.1471 0.4031 0.7628 1.2484 2 3 6 9 8

C -1.6214 0.3157 N/A 1.9758 0.8732 0.5879 0.0583 0.2041 -0.0304 0.3408 0.7314 1.0261 1 3 7 6 9

D -1.6679 -2.0533 -1.9758 N/A 0.8916 0.1686 -0.3531 -0.1562 -0.4051 0.0415 0.6074 -0.7885 6 3 2 21 4

E -0.9212 -0.9074 -0.8732 -0.8916 N/A -0.2954 -0.8669 -0.7647 -0.8207 -0.4032 -0.6383 -1.1447 10 1 0 31 1

F -0.6156 -0.5227 -0.5879 -0.1686 0.2954 N/A -2.1129 -2.3104 -1.4558 -0.3442 0.1392 -0.2713 7 4 0 25 3

using historical data q(i, t − 1) and the new estimate r(i, t). 6) Use Eq. (4) to determine the region R(t). 7) If maxi∈Ω\N (R(t−i−1)) q(i, t) > q(R(t − i), t − i) for i = 1, 2, . . . n, where A\B means the set of elements in A but not in B, let R(t) = arg maxi∈Ω\N (R(t−1)) q(i, t). 8) Output R(t).

G -0.2744 0.0256 -0.0583 0.3531 0.8669 2.1129 N/A 1.6331 -0.8015 1.2917 0.873 0.4272 1 3 7 6 9

H -0.3375 -0.1963 -0.2041 0.1562 0.7647 2.3104 -1.6331 N/A -2.3453 1.7792 0.7563 0.132 3 4 4 13 7

I -0.2456 0.1471 0.0304 0.4051 0.8207 1.4558 0.8015 2.3453 N/A 1.6949 0.7628 0.4198 0 3 8 3 11

J -0.4982 -0.4031 -0.3408 -0.0415 0.4032 0.3442 -1.2917 -1.7792 -1.6949 N/A 0.3656 -0.1569 6 2 3 20 5

K -0.896 -0.7628 -0.7314 -0.6074 0.6383 -0.1392 -0.873 -0.7563 -0.7628 -0.3656 N/A -1.2635 9 1 1 28 2

L -1.6018 -1.2484 -1.0261 0.7885 1.1447 0.2713 -0.4272 -0.132 -0.4198 0.1569 1.2635 N/A 5 3 3 18 6

when the tags move. Further investigation on this issue is needed.

IV. N UMERICAL R ESULTS In this section, we present the numerical results on the indoor regional localization system developed in Section III, including the accuracy, the delay, the scalability, and the energy saving. A. Accuracy We let an occupant take 4 tags and walk around in the 12 regions and record 60 RSSI vector samples per tag for each region. So we have 240 samples for each region. We use the algorithm in Section III to estimate the region. The probability for the algorithm to locate a tag to the correct region is shown in Fig. 4 for each region, respectively. The average probability is 93.0% with a standard deviation of 5.8%. If we allow to locate to the correct region or the neighboring region, the accuracy of the algorithm is also shown in Fig. 4, with an average value of 99.3% and a standard deviation of 1.5%. Note that we do observe a much lower localization accuracy if the tag does not move but is placed at a fixed position. One possible reason for this phenomenon is that due to the multipath effect, there could be large fluctuations in the RSSI if the tag moves a little bit (say 10 centimeters). When the tag moves, the RSSI received by the reader may be the average of the RSSI in the neighboring positions, which smoothes the fluctuation. But when the tag is placed at a specific position, the RSSI received by the reader is usually different from the average RSSI if the tag moves in the neighboring positions. So, one possible way to overcome this difficulty is to use tags with acceleration sensors and thus send RF signal only

Fig. 4.

The localization accuracies of the 12 regions.

B. Delay The delay of the localization system is defined as the difference between the time when the occupant moves to a region and the time when the system output the correct location. This delay is caused by both the hardware and the software. The hardware delay consists of the processing time of the RSSI at the readers, the time to transmit the RSSI to a serial-to-parallel-port converter, and the time for the converter to transmit the RSSI to the computer where the localization algorithm is running. The software runs very fast and the calculation time can be ignored. But it takes time for the localization that is output by the software to move to the correct region. This delay is mainly caused by the discounted factor a used in Eq. (2) when incorporating the


historical data. By adjusting the buffer size in the reader, the converter, and the value of the discount factor, the delay can be changed. Numerical results show that in our system the hardware delay is between 5 to 6 seconds, and the software delay is between 2 to 3 seconds, which gives an overall delay of 7 to 9 seconds. C. Scalability We test the scalability of our localization system from two aspects. First, assume the ratio of the number of readers to the number of regions is 1/2 (in the current system this ratio is 7/12). For the localization algorithm to finish the calculation before the next RSSI vector is input to the algorithm, we used a computer with 1.66GHz CPU and found that the system can support up to 24 readers and 48 regions. Note that if a building has a large floor so that many readers are deployed, due to the concrete walls that separate the rooms, the number of readers that can receive the RF signals from a same tag is usually small. In that case, we can group the readers and run the localization system in parallel in different sub-regions of the floor. In words, our localization system can do regional localization for a large floor. Second, we try to reduce the number of readers without much performance degradation in localization accuracy. As is shown in Fig. 5, when we remove reader R1 and R7, the average localization accuracy is still over 85%.

occupants. The regional localization information is stored in a database [10], which can be used to control the lights, the HVAC, and the fabrics, just to name a few. Numerical results show that only 60% of the regions contain occupants during the working time. By assuming all the lights are on during the working time, this leads to a 40% potential energy saving. V. C ONCLUSION In this paper we develop a regional localization system using active RFID for an indoor environment. We use multiple tag-readers to handle the multipath effect, use regional localization instead of point-to-point localization to increase the robustness to the changing environment, use round-robin comparison to locate the occupant into one of the multiple regions, and incorporate the geometric and historical data to further improve the localization accuracy. The system localizes the occupant to the correct region with an average accuracy of 93.0%, and localizes to the correct region or the neighboring regions with an average accuracy of 99.3%. This indoor occupancy localization information can be used to control the lights, the HVAC, and the fabrics to save the energy consumption and to increase the livelihood of the building. Because the received RSSIs of moving tags are usually different from the static tags, some existing method such as LANDMARC cannot be applied directly to this system. It is also of interest to study how to incorporate the human behavior model into our system to achieve higher localization accuracy. R EFERENCES

Fig. 5. When the number of readers is reduced, the average localization accuracy decreases. But when two readers are reduced, the average localization accuracy is still no less than 85%.

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D. Energy Saving The experimental scene in Fig. 1 is an office floor. We use the localization system to locate the position of the
