Reliability improvement for an RFID-based psychiatric patient localization system q,qq

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1 Available online at Computer Communications 31 (2008) Reliability improvement for an RFID-based psychiatric patient localization system q,qq Chieh-Ling Huang a,b, Pau-Choo Chung a,b, *, Ming-Hua Tsai a,b, Yen-Kuang Yang c, Yu-Chia Hsu d a Department of Electrical Engineering, National Cheng-Kung University, 1 University Road, Tainan 70101, Taiwan, ROC b Institute of Computer and Communication, National Cheng-Kung University, Tainan 70101, Taiwan, ROC c Department of Psychiatry, National Cheng Kung University, Medical College, 138, Sheng-Li Road, Tainan 704, Taiwan, ROC d Advanced e-commerce Institute, Institute for Information Industry, 22F-A, 333 Sec. 2 Duenhua S. Road, Taipei 106, Taiwan, ROC Received 7 March 2007; accepted 6 January 2008 Available online 16 January 2008 Abstract Collaboration between Field Generators, Readers and Tags generates the required functions in using radio frequency identification (RFID) for psychiatric patient localization. The Field Generator continually transmits trigger signals within its transmission range. When a Tag, which a patient wears, receives a trigger signal from a Field Generator, the Tag responds to a Reader with its own ID and ID of the Field Generator that issues the trigger. Based on Field Generator IDs that a Tag responds to, the location of the patient wearing the Tag can be estimated approximately. However, in recent RFID based localization systems, certain phenomena impact the reliability of signal transmission and decrease the feasibility of the localization system. To eliminate interference from different Field Generators, this study presents a novel graph coloring with merging and deletion (GCMD) algorithm for such a localization system that eliminates interference from Field Generators located near each other. Experimental results indicate that the GCMD algorithm significantly improves localization system reliability, thereby increasing the feasibility of RFID for psychiatric patient localization. Ó 2008 Elsevier B.V. All rights reserved. Keywords: RFID; Localization; Graphic coloring; Reliability 1. Introduction Psychiatric patients often cannot control their actions, occasionally resulting in dangerous behavior. Therefore, q The authors are corresponding with the Smart Media and Intelligent Living Excellence (SMILE) Lab. qq This work was supported by the Applied Information Services Development and Integration project of Institute for Information Industry and National Science Council, Taiwan, ROC, under Grant NSC E * Corresponding author. Address: Department of Electrical Engineering, National Cheng-Kung University, 1 University Road, Tainan 70101, Taiwan, ROC. Tel.: x62373; fax: / addresses: kaio@ee.ncku.edu.tw (C.-L. Huang), pcchung@ee. ncku.edu.tw (P.-C. Chung), q @mail.ncku.edu.tw (M.-H. Tsai), ykyang@mail.ncku.edu.tw (Y.-K. Yang), ychsu@iii.org.tw (Y.-C. Hsu). continuously monitoring patients is crucial to treating psychiatric patients. For patients with relatively severe psychiatric disorders, care centers are regarded essential. Effectively caring for of such psychiatric patients should include protecting patients from danger, being aware of treatment progress, and preventing patients from leaving a care center. To achieve these care objectives, radio frequency identification (RFID) devices, which are small and relatively cheap, can effectively monitor the location of psychiatric patients. RFID technology has been utilized in various applications, including supply chain management [1], entry and exit control [2]. Such applications have the same attributes, i.e., the monitored objects cannot move of freely. Localizing moving objects, e.g., freight localization or human localization [3] is a challenging and relatively unexplored task. Interference and miss detection are challenges /$ - see front matter Ó 2008 Elsevier B.V. All rights reserved. doi: /j.comcom

2 2040 C.-L. Huang et al. / Computer Communications 31 (2008) typically associated with localizing moving objects. Interference decreases reliability in using RFID for position localization. Several studies have focused on hardware and software when investigating RFID reliability [4]. Investigating antenna design based on free space architecture [5,6] originates from a hardware perspective. Experiments have demonstrated that a passive RFID system has an extremely limited operational range, which can be further decreased due to morphology distortion. In certain applications in which Tags are placed on objects, bending object surfaces can also result in degradation of signal propagation, therefore causing poor reliability. In this scenario, an antenna structure with minimal sensitivity to morphology distortion should be utilized [7]. On the other hand, integrating time hopping code division multiple access (CDMA), cyclic redundacy check (CRC) error detection and repetition of transmission has been utilized to eliminate the collision problem [8]. The Colorwave algorithm [9] is a simple, distributed and on-line algorithm for the Reader collision problem in RFID systems. Pure ALOHA [10] and slot ALOHA [11] are common methods that use repetitive transmission to improve successful transmission probability. Pure ALOHA uses the Poisson model to evaluate the time interval between two repetitive transmissions. The slot ALOHA utilizes Markov probability to derive the number of time slots and response time required to attain a predetermined successful transmission rate. Experimental evidence demonstrated that a reduction in reliability caused by Field Generator interference is an important issue when using RFID to localize psychiatric patients. To resolve this problem, this paper proposed a graph coloring with merging and deletion (GCMD) algorithm for scheduling operation of Field Generators. The GCMD algorithm transforms deployment of Field Generators into a graph with vertices representing Field Generators and each edge between two vertices representing a signal range overlapping between two associated Field Generators. The coloring algorithm is then employed to cluster the Field Generators into groups. Subsequently, the time division multiple access (TDMA) algorithm can be utilized to schedule the operating slots for each group, such that each group operates in a mutually exclusive non-overlapped period. Consequently, the vertices must be classified into the smallest number of groups possible. To attain this aim, this study proposes merging and deletion of vertices based on a Field Generator relationship criterion, such that the graph can be effectively simplified. Thus, the number of groups is minimized, and system performance and reliability are improved. The remainder of this paper is organized as follows. First, the RFID-based psychiatric patient localization system is described in Section 2. Section 3 describes the GCMD scheduling approach. The criteria developed for merging and deletion are also introduced. Section 4 presents experiment descriptions and experimental results. Localizing reliability comparisons with and without the proposed scheduling algorithm are demonstrated. Finally, conclusions are presented in Section System overview The Department of Psychiatry, National Cheng Kung University Hospital (NCKUH) uses an RFID-based psychiatric patient localization system [3]. The department at NCKUH is the primary center for medical treatment of psychiatric patients in southern Taiwan. The department is located in its own building directly across from the NCKUH. The second floor serves as a clinic for psychiatric patients, and the third floor is an activity area. Psychiatric patients are brought to the building every morning and picked up in the afternoon. Nurses are responsible for scheduling daily activities and providing basic care; doctors are responsible for medical treatment. This RFID-based psychiatric patient localization system uses a ultra high frequency (UHF) long range tracker. The Field Generators operate on 433 MHz when triggering the Tag to respond, and the Tag replies to a Reader with 916 MHz signal once it is triggered. The entire care center is divided into several regions. Each Field Generator is responsible for one region; the coverage for regions overlaps. Fig. 1 presents the deployment of the six Field Generators and one Reader on the first floor of the care center and the grounds. Based on this deployment, Field Generators FG 4, FG 2 and FG 1 together track the rout from the exit to the ground area and main gate. Thus, when psychiatric patients move from Field Generator FG 4 to Field Generator FG 1, or Field Generator FG 2 to Field Generator FG 1, the system signals an alarm to the manager. Fig. 1. Dispose diagram of RFID-based psychiatric patient localization system.

3 C.-L. Huang et al. / Computer Communications 31 (2008) Psychiatric patients in the care center wear watch-like Tags. When patients are within a transmission region of a Field Generator the Field Generator triggers the Tag, and the Tag then transmits information, including Tag ID and Field Generator ID, to the Reader. The Reader then sends the information to a central server, which aggregates information from Readers, that determines the location of a given Tag, and, thus, the patient. Finally, this location information is recorded in a database. To localize patients effectively, the Field Generators must appropriately placed. In this experiment, the care center is on the first floor of the building and the Field Generators are placed on the first floor near the staircase on the first floor, the first floor building exit and around the grounds, and near exit from the grounds to the street. Via the Field Generators in the staircase and at the building exit, patients descending the stairs to the ground area are detected. The Field Generators in the ground area are also used to determine whether the patient is closing the main gate. Thus, the system can monitor patients attempting to leave the grounds. The relative density of Field Generators and reference Tags also computes patient positions and acquires data of patient activity patterns. Statistical analysis of activity patterns provides valuable data for evaluating treatment progress. 3. Reliability improvement with field generator scheduling This RFID-based system for localizing psychiatric patients relies on the Tag correctly receiving signal from the Field Generators to estimate the Tag location. However, two Field Generators with overlapping transmission ranges simultaneously issuing trigger signals to a Tag causes signal interferences in the overlapped region. This interference results in loss of signal and, therefore, decreases localization accuracy. Resolving this problem is necessary for accurate localization. In this section, a GCMD scheduling technique is presented that optimally schedules Field Generators such that overlapping Field Generators operate in mutually exclusive patterns. In this scheme, Field Generator deployment is translated into a graph, and a coloring algorithm is applied to this graph to partition the Field Generators into the minimal number of groups. This partitioning guarantees that Field Generators within the same group do not interfere with each other. A TDMA time division scheduling scheme is applied to groups of Field Generators for alternating the time slots within which each group operates. Thus, when one Field Generator group is operating, the other groups cannot operate. Maintaining the smallest number of Field Generator groups is essential, as the smallest number of groups allows the highest number of Field Generators to operate at any given time. Thus, increased system efficiency can be attained. This study presents a novel merging and deletion scheme that reduces the number of vertices in a graph. Thus, graph size can be reduced and the number of obtained partition groups decreased. The GCMD procedure is described as follows. Fig. 2 presents a flow chart of the GCMD procedure Transform the relationships among all Field Generators into a graph First, the relationships among Field Generators are transformed into an undirected graph G, whereas V and E are sets of vertices and edges, respectively. Where V Fig. 2. The flow chart of GCMD algorithm.

4 2042 C.-L. Huang et al. / Computer Communications 31 (2008) and E are derived based on the Field Generators and their signal region overlapping situations, respectively. 1. Set of vertices (V): Every Field Generator is associated with a vertex in the graph, e.g., vertex iv i represents Field Generator FG i. Then the localization system has Field Generators FG 1,FG 2,... and FG n, and the set of vertices V contains elements of vertices v 1,v 2,... and v n. Thus, the set of vertices for graph G is V(G)= {v 1,v 2,...,v n }. 2. Set of edges (E): Let S u represent the signal region of Field Generator FG u. When any two Field Generators have overlapping signal regions, their associated vertices are connected by an edge. For example, let sets S u and S v represent the signal regions of Field Generators FG u and FG v, respectively. If Field Generators FG u and FG v have overlapping regions, that is S u \ S v 6¼ / (empty set), then there is an undirected edge (u,v) between v u and v v. All these edges comprise the set of edges in graph G as E(G). Thus, all Field Generators and their overlapping signal ranges are transformed into an undirected graph G =(V,E) Vertex deletion and merging Once the graph is constructed, this step reduces the number of vertices in the graph via deletion and merging based on certain criteria Vertex deletion When the range of one Field Generator, FG x,iscompletely covered by other Field Generators, the function of FG x can be replaced by a combination of these other Field Generators. In this scenario, the Field Generator FG x can be removed. Therefore, the vertex deletion is as follows. Theorem 1. Let G be a graph associated with the RFID Field Generator deployment. Let v x be a vertex in graph G with adjacencies v x1,v x2,...,v xp. When S x S x1 [ S x2 [...S xp, then v x can be deleted Vertex merging From the previous discussion, we concluded that two Field Generators with overlapping signal regions are prohibited from transmitting signals simultaneously. However, consider a situation in which the entire overlapping region is covered by a union of Field Generators. In this scenario, other Field Generators can cover the overlapping region, thus enabling these two adjacent Field Generators to simultaneously transmit signals. Consequently, the two vertices associated with the two Field Generators can be merged, and no special care is required to avoid signal interference from the two Field Generators. Thus, Theorem 2 is developed to merge the two adjacent vertices. Theorem 2. If v u and v v are adjacency and have the same adjacencies v k1,v k2,...,v kq that satisfy, (S u \ S v ) S k1 [ S k2 [...\ S kq then v u and v v can be merged to v uv with S uv defined as S uv =(S u [ S v ) (S u \ S v ) Vertex coloring Following vertex deletion and merging, this study obtains a trimmed graph representing Field Generator deployment. Then, a coloring algorithm is applied to the trimmed graph, in which connected vertices are assigned different colors. The Field Generators with the same color are assigned to the same group, and, therefore, can transmit signals simultaneously. Conversely, the Field Generators with different colors are assigned to different groups; scheduling must be applied to avoid signal conflict Operation slot allocation After groping Field Generators, a weighted TDMA is applied to assign time slots for operation to each group. Consider that each partitioned group can occupy different levels of importance. For example, one group can cover a crucial region such as dangerous areas or exits. Thus, this group should be allocated more time slots than other groups. Another consideration is the size of an area covered by a group of Field Generators. Group importance should be assigned based on the size of the area covered by a group of Field Generators. Consequently, the importance factor for each group w i can be approximated. Then based on importance factors, w i, the time slot ratio for each group can be computed as T i ¼ P w i n. Thus, the k¼1 w k slot partition for each group can be based on factors of T i s. 4. Experiments For elucidate system performance, the maximal signal range of each Field Generator in the experimental environment is measured. Fig. 3 illustrates the signal range of each Field Generator. Fig. 4 presents a scenario in which Field Generator ranges overlap. Based on the overlapping scenario shown in Fig. 4, without graph reduction (vertex deletion and merging), the relationship among Field Generators is transformed into a graph (Fig. 5(a)). After the graph coloring is applied, the group results of the Field Generators are shown in Fig. 5(b). In Fig. 5(b), the Field Generators are divided into three groups: group 1 (Field Generators FG 1 and FG 3 ); group 2 (Field Generators FG 2 and FG 5 ); and, group 3 (Field Generators FG 4 and FG 6 ). However, from Fig. 5(a), we can find that vertex v 3 has adjacent vertices v 2, v 4, v 5 and v 6. Additionally, based on a signal range of each Field Generator shown in Figs. 3 and 4, the signal range of Field Generator 3, S 3, is contained within the union of S 2, S 4, S 5 and S 6 ; that is,

5 C.-L. Huang et al. / Computer Communications 31 (2008) Fig. 3. The signal distribution range of each Field Generator. Fig. 4. The mixed state signal situation of all Field Generators. Fig. 5. The graph of associated Field Generators without graph reduction. (a) Graph obtained. (b) Coloring result. S 3 S 2 [ S 4 [ S 5 [ S 6. Thus, according to Theorem 1, vertex v 3 can be deleted from the graph. Thus, after vertex deletion is applied to the graph, the graph of Fig. 6(a) can be obtained. Fig. 6(a) reveals that vertex v 1 and vertex v 4 have the same adjacency, v 2. Also, based on the signal range of each Field Generator shown in Figs. 3 and 4, we have S 1 \ S 4 S 2. Then, according to Theorem 2, vertex v 1 and vertex v 4 are merged into vertex v 1,4. Thus, after vertex merging is applied to the graph, the graph is finally simplified to Fig. 6(b). Applying the coloring algorithm to Fig. 6(b), we can obtains the coloring result of Fig. 6(c), in which Field Generators are divided into two groups: group 1 containing Field Generators FG 1, FG 4 and FG 6 ; and, group 2 containing Field Generators FG 2 and FG 5. On the other hand, as noted, when the coloring algorithm is applied to the original graph without graph reduction, the coloring algorithm requires three groups to partition. Thus, via graph reduction, a reduced number of groups is obtained, and, therefore, the number of Field Generators that can operate simultaneously is increased. In the current RFID system, Tags can be programmed to send out responses periodically (reciprocated regularly) or only when triggered by Field Generators. In this psychiatric patient localization system, a patient s location is computed based on the communication range of the patient s Tag within the Field Generators with respect to ranges of reference Tags. Consequently, Tags are pro-

6 2044 C.-L. Huang et al. / Computer Communications 31 (2008) Fig. 6. The graphic after graph reduction. (a) After vertex deletion. (b) After vertex merging. (c) Coloring result. grammed to report only in response to a Field Generator s trigger. Another issue that requires consideration is increasing the Reader acceptance rate in the scenario in which Tags may generate interference when more than two Tags are sending reports back to the Reader simultaneously. When localizing moving objects, it is almost impossible to schedule Tags as a result of their highly dynamic movement. Currently the most commonly adopted and effective technique for resolving Tag interference is through repetitive transmission. The ALOHA [12] indicates that with sufficient number of transmissions, the ALOHA system achieves a high success rate. Repetitive transmission times in the system are set at 6, and the associated lasting time is T rep. Another parameter associated with a Tag is time in field (TIF) time. Consider the situation in which a Reader receives two consecutive reports from the same Tag. How can the Reader determine whether the two reports are issued due to two separate triggers, or whether the two reports are due to a repetitive response trigger? To resolve this problem, a Tag can be programmed with a TIF Time (T TIF ) that specifies the time duration before the Tag can be triggered again. Another aim of TIF time is to prevent Tags from wasting energy replying to the same trigger from a Field Generator. In these experiments, if a Field Generator continuously sends a trigger signals, in an optimal situation, the Tag is physically trigged once for every T rep + T TIF time period. Thus, every T rep + T TIF is defined as one round. As known, a Tag repeats its response six times. Assume that within these six responses, if one of the six responses in one round is received, this round is regarded as successfully received. Otherwise, this round is considered lost. To measure the reliability value, we define the lost rate of responses L as the total number of lost rounds divided by the total number of rounds that should trigger the Tag: r L ¼ 1 ð1þ T =T rep þ T TIF where r denotes the number of rounds that the Reader successfully received Tag s reply signal and T represents time cost. In this system, it takes roughly three rounds for a patient to move from the building exit to the main gate. Under this scenario, we define response rate R n as: R n ¼ 1 L n ð2þ where n is the number of rounds 3 in this case. To measure reliability improvement using GCMD scheduling, two experimental situations are devised: (1) a fixed-points test and (2) a route test Fixed-points test This experiment is utilized to test the reliability of representative fixed points in the RFID-based localization system with and without the proposed GCMD scheduling. The selected representative fixed points are at the centers of covering regions for all Field Generators, and at the centers of overlapping regions. Fig. 4 presents the covering ranges of the six Field Generators deployed on the first floor. In this scenario, there are 15 fixed positions according to the deployment architecture (Fig. 7). Among these fixed positions, the positions 1 6 reside in single Field Generator range, whereas positions 7 10 are located in the overlapping region of two Field Gen- Fig. 7. Positions for fixed-point test.

7 C.-L. Huang et al. / Computer Communications 31 (2008) Table 1 Results of fixed-points test for the system without scheduling Point number Point number Point number Averaged response Table 2 Results of fixed-points test when GCMD algorithm is applied without graph reduction Point number Point number Point number Averaged response erators; positions are in the overlapped region for three Field Generators; position 15 is in the overlapping region of four Field Generators. These 15 positions taken together comprise various situations for a fixedpoint test. In the fixed-points test, people wearing Tags stand at each fixed position for 1 min; the response rate for each position is computed according to Eq. (2). Table 1 presents the response rate for each fixed point for the system without scheduling, which has a very low average response rate (43.28%). The Field Generator deployment is transformed into a graph. Without graph reduction, namely, vertex deletion and merging, the vertices are partitioned into three groups. The TDMA is then applied to alternately divide time periods into the three Field Generator groups; each group operates for 1 s and the sleeps for 2 s. Table 2 presents the measured response rates for the 15 fixed points. A comparison of fixed-point test results for scheduling (Table 2) with those without scheduling (Table 1) indicates that response rates are dramatically increased following scheduling. In the following experiment, vertex deletion and merging is applied to the graph, followed by the vertex partition. Thus one vertex, v 2, is deleted, and, finally, two groups are acquired for the remaining vertices. Thus, the time periods alternate between the two groups of Field Generators; each Field Generator is in operation for 1 s followed by 1 s of sleep. Thus, the rate of transmission time for each Field Generator is increased from 1/3 to 1/2. Table 3 shows Table 3 Results of fixed-points test when GCMD algorithm is applied Point number Point number Point number Averaged response Table 4 Results of fixed-points test when GCMD algorithm is applied with weighted TDMA Point number Point number Point number Averaged response 86.44

8 2046 C.-L. Huang et al. / Computer Communications 31 (2008) Fig. 8. The routes for reliability test. (a) The route connecting 15 representative points. (b) The route connecting the lowest reliability points. (c) The route connecting the highest reliability points. (d) The route having the shortest path from exit to main gate. (e) The route tracing a region not tested in (a d). the response rates in this experiment. The results obtained without vertex deletion and merging (Table 2) are compared with those applying vertex deletion and merging (Table 3). This comparison reveals that the overall response rate has increased significantly, except for positions 3, 8 and 14 that dramatically decreased. Closely examining these positions indicates that all three positions are either in corners, or very close to building walls (Fig. 7). Further investigation of these conditions is worth of future study. Consider that the signal range area of Field Generator FG 1 (in group 1) covers an entrance and exit, which are very more important. Additionally, group 1 (Field Generators FG 1,FG 4,FG 6 ) covers an area significantly larger than that covered by group 2 (Field Generators FG 2, FG 5 ). Therefore, the rate of allocated transmission time for group 1 is two times that of group 2. Thus, in this experiment, group 1 is assigned 2 s for operation and group 2 is assigned 1 s. Table 4 presents the measurement results for fixed points following this adjustment. Comparing the results after applying weighted TDMA (Table 4) with that apply traditional TDMA (Table 3), it can be observed that the average response rate is increased. Moreover, the response rate of point 1, i.e., nearest to an entrance and exit, is increased to 99.9% Route test Given that patients wearing Tags may stroll around inside the care center, this study also performs reliability tests of this localization system when the patients are strolling around. As such, several routes represent the critical paths designed. Fig. 8 presents the experimental routs, where (a) Route1 is the path passing the 15 representative points, (b) Route2 is the path connecting with poor reliability in the fixed-point test, (c) Route3 is the path connecting points with high reliability, (d) Route4 is the path of shortest distance from the building exit to the main gate and (e) Route5 is a route tracing through a region that is rarely covered by routes (a d). During experiments, people

9 C.-L. Huang et al. / Computer Communications 31 (2008) wearing Tags walk or run along each experimental path. The response rate is computed for each experimental route. Table 5 presents the response rates when the people walk or run on the five routes for the system without scheduling. Among these five routes, Route2 connects the points of the lowest response rate. Thus, according to Table 5, that Route2 has the lowest response rate among the five routes for the walking and running models. Furthermore, given the interferences of signal collision, some routes have rather low response rates. Reducing these interferences involves applying a scheduling to the Field Generators associated with Fig. 5(a). Table 6 presents the response rates after the scheduling algorithm is applied. All response rates have clearly improved significantly. However, as mention earlier, the scheduling on Fig. 5(b) causes the rate of transmission time for each Field Generator to be 1/3. Also mentioned earlier, the GCMD can improve the rate of transmission time from 1/3 to 1/2 in this case. Table 7 illustrates the response rates when GCMD is applied. Comparing the results in Table 6 with those in Table 7 reveals that response rates in the running model are reduced and the walking model is increased following graph reduction in GCMD scheduling algorithm. It may be noticed that graph reduction does not improve response rate. This is not surprising as the functionality of graph reduction is not to increase the response rate, but rather reduce the graph size; consequently a small Table 5 Results of routes test for the system without scheduling Walking model Running model Route number Route number Average response Overall average response Table 7 Results of routes test when GCMD algorithm is applied Walking model Running model Route number Route number Average response Overall average response number of excluded groups are obtained and additional Field Generators can be operate. As mentioned in fixed-point test, the Field Generators in group 1 in this scenario play more critical roles since their signal ranges covers a significantly large area where includes an entrance and exit points. Therefore, the rate of allocated transmission time for group 1 is adjusted to two times that of group 2. Table 8 presents the measurement results of the route test when applying the weighted TDMA. Based on experimental results, the response rates of the RFID-based localization system are significantly enhanced when GCMD scheduling is applied. However, position 3 has very low response rate with and without the GCMD algorithm. Analysis indicated that position 3 is in the furthest corner in this environment (Fig. 7). The Field Generator has difficulty reaching this sharp corner and the Tag cannot reach the Reader. Thus, a Reader is added at position 10. Experimental results indicate that by adding a Reader, response rate for position 3 improves from 0% to 57.81% in the unscheduled original system, and from 14.26% to 83.36% using GCMD scheduling. Notably, transmission time slots should be based on group importance. For group covering important regions or large areas should be allocated increased time periods. Adopting such strategies effectively increases localization system performance. Finally, Fig. 9 presents a summary Table 6 Results of routes test when GCMD algorithm is applied without graph reduction Walking model Running model Route number Route number Average response Overall average response Table 8 Results of routes test when GCMD algorithm is applied with weighted TDMA Walking model Running model Route number Route number Average response Overall average response 93.80

10 2048 C.-L. Huang et al. / Computer Communications 31 (2008) Fig. 9. Reliability comparison for original system and the system with proposed GCMD algorithm. of comparisons of response rates between the original system without GCMD scheduling and the system with GCMD scheduling. 5. Conclusion The RFID devices that are small and relatively cheap are very appropriate for use in localizing psychiatric patients. However, certain phenomena degrade the reliability of signal transmission and decrease feasibility of the localization system. In this study, a GCMD scheduling model is utilized for scheduling Field Generator transmissions in an RFID-based psychiatric patient localization system, thereby reducing interference caused by Field Generators located near one another. The GCMD scheduling scheme transforms relationships among Field Generators into an undirected graph; this graph is then simplified using deletion and merging algorithm proposed herein. Once the graph has been simplified, the GCMD algorithm colors the graph vertex and a weighted TDMA is applied to assign time slots for operation to each group. Experimental results demonstrated that the system is highly effective when using the proposed scheduling algorithm. Acknowledgements This work was supported by the Applied Information Services Development and Integration project of Institute for Information Industry. The authors thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC E References [1] K. Michael, L. McCathie, The Pros and Cons of RFID in supply chain management, International Conference on Mobile Business (2005) [2] F. Wu, F. Kuo, L.W. Liu, The application of RFID on drug safety of inpatient nursing healthcare, in: ACM Seventh International Conference on Electronic Commerce, vol. 113, 2005, pp [3] M.H. Tsai, C.L. Huang, P.C. Chung, Y.K. Yang, Y.C. Hsu, S.L. Hsiao, A psychiatric patients tracking system, IEEE International Symposium on Circuit and Systems (2006) [4] D.H. Shih, P.L. Sun, D.C. Yen, S.M. Huang, Taxonomy and survey of RFID anti-collision protocols, Computer communications 29 (2006) [5] P.R. Foster, R.A. Burberry, Antenna problems in RFID systems, IEE Colloquium on RFID Technology, 1999, 3/1 3/5. [6] J. Siden, P. Jonsson, T. Olsson, G. Wang, Performance degradation of RFID system due to the distortion in RFID tag antenna, in: 11th International Conference on Microwave and Telecommunication Technology, 2001, pp [7] X. Zhou, G. Wang, Study on the influence of curving of tag antennas on performance of RFID system, in: Fourth International Conference on Microwave and Millimeter Wave Technology, 2004, pp [8] Y. Fukumizu, M. Nagata, S. Ohno, K. Taki, A design of transponder IC for highly collision resistive RFID systems, in: IEEE Asia-Pacific Conference on Advanced System Integrated Circuits, 2004, pp [9] J. Waldrop, D.W. Engels, S.E. Sarma, Colorwave: an anticollision algorithm for the reader collision problem, IEEE International Conference on Communications 2 (2003) [10] B. Zhen, M. Kobayashi, M. Shimizu, To read transmitter only RFID tags with confidence, in: 15th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 1, 2004, [11] H. Vogt, Multiple object identification with passive RFID tags, IEEE International Conference on Systems, Man and Cybernetics 3 (2002). [12] A.S. Tanenbaum, Computer Networks, fourth ed., Prentice Hall PTR, New Jersey, 2003, pp