AbstractâLocation and tracking of healthy or infirm elders is a potentially useful application as society aging is accelerating worldwide and as technologies such ...
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
An Energy-efficient Elderly Tracking Algorithm Xiaoming Xiao, Albert Kai-sun Wong, Kam Tim Woo, and Roger Shu-Kwan Cheng Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Abstract—Location and tracking of healthy or infirm elders is a potentially useful application as society aging is accelerating worldwide and as technologies such as cellular positioning and Assisted Global Positioning System (AGPS) are maturing. For a wearable device to be used in such an application, the battery operating hour is often a key consideration. This paper describes an energy-efficient cellular and AGPS tracking algorithm based on the concept of Personal Common Location (PCL) that we define as the locations at which an elder spends most of his/her time, as recognized from historic cell ID record. During the tracking process, AGPS fixes are avoided if the elderly carrier is determined to be located within a PCL, if the destination PCL can be predicted, or if AGPS signals are likely to be unavailable. This paper also describes our approach for off-line PCL recognition, the necessity of cell ID clustering, as well as the results from our tracking experiments.
organizations that we worked with have expressed their requirement of a MS that can work continuously for 10 days or more without a battery recharge. Therefore, we are motivated to design a dynamic algorithm for scheduling AGPS fixes and location report, so that AGPS fixes are avoided when they are not needed and performed frequently when they provide useful information. Our tracking algorithm aims to utilize prior knowledge on the mobility patterns of the elder, and to take advantage of the support of an on-line server.
Index Terms—Assisted Global Positioning System (AGPS), energy efficiency, elderly tracking, intelligent location fix
I.
INTRODUCTION
Elderly location and tracking is an emerging application in many aging societies including Hong Kong to support the independent living of the elders with different degrees of diminished capabilities. It can provide alarms to the caregivers upon the detection of unusual behaviors and a general indication of an elder’s well-being through analyzing his/her daily location profiles and activity patterns. We have previously implemented a prototype elderly tracking system based on a commercialized 3G cellularenabled AGPS tracker completed with our own AGPS server, reference station for the collection of GPS satellite data, and application server [1]. The architecture of this tracking system is shown in Fig. 1. AGPS fixes are scheduled at a fix interval of 5 to 15 minutes in this prototype system. We have also proposed a path reconstruction algorithm for enhancing the visualization of an elder’s mobility patterns [2]. In this paper, we describe an elderly tracking algorithm aimed to extend the battery operating hours of the mobile tracker, which we will generically call the mobile station (MS). As we described previously, AGPS is more energy-efficient and provides faster response time than stand-alone GPS in cold-start scenarios, making it advantageous for people tracking. But our previous research has also shown that there is still a significant cost in terms of battery lifetime for each AGPS fix, particularly for a commercialized mobile tracker that has been optimized to draw minimum current in standby. Social service
Fig. 1 Architecture of Prototype AGPS Elderly Tracking System
It is expected that many elders in Hong Kong typically spend most of their time in a set of locations found commonly in different days. These locations may be the elder’s home, parks, elderly centers, and dining and shopping areas. We define Personal Common Locations (PCLs) as the locations where a specific elder spends most of his/her time. In the offline phase of our algorithm, historic data is first collected for learning, or recognition, of the set of PCLs for an elderly carrier. In the on-line phase of the algorithm, the current mobile network cell ID is first detected and matched with those associated with the PCLs to produce the prior knowledge in our tracking algorithm. It has been shown in previous research [3, 4] that the accuracy of localization via cell ID may not always satisfy people-tracking requirements such as E-911. But localization via cell ID has the advantages of low cost, easy implementation, and high energy efficiency.
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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
Our algorithm is based on the premise that when an elder is estimated to be in a familiar location which is a PCL, exact localization at GPS level is not necessary. Previous research [5, 6] has also shown that the cell ID transition can be used to predict the moving direction of a person. Therefore, our algorithm includes a destination PCL prediction process that is executed at the server while the carrier is detected to be outside the PCLs. When the destination PCL can be predicted confidently based on the cell ID transition sequence, AGPS fixes are also avoided. II.
which should be a relatively small set, is kept at the MS. In the PCL matching process, the MS determines if the observed current cell ID is in CCID.
STATE TRANSITION DIAGRAM
We first describe the on-line phase of our algorithm. We propose to address only four mobility situations of the elder under tracking, as shown in Table I. Choujaa and Dulay [7] proposed using a hierarchical model for human activity recognition. We employ a similar idea and design a hierarchical state transition diagram with two super-states, as shown in Fig. 2. The four states in Fig. 2 correspond to the four tracking situations. The actions to be taken by the MS and by the server in each state, and the trigger events (i.e. E1 to E7) for transitions between states are described in Fig. 2. The same state is maintained by both the MS and the server. TABLE I SUMMARY OF TRACKING SITUATIONS Situation Description 1 The carrier is in a recognized PCL 2 The carrier is moving among recognized PCLs 3 The carrier is at unknown locations where GPS signals are available 4 The carrier is at unknown locations where GPS signals are not available In Fig. 2, the COMMON super-state is designed for situations when the elderly carrier is in a PCL or moving among PCLs (i.e. Situation 1 & 2). Since the elderly carrier is following a familiar life pattern here, energy-saving tracking is performed by disabling AGPS and resorting to cellular positioning alone. The actions taken in this super-state include on-line cell ID matching and destination PCL prediction. The MS is expected to spend most of the time in this super-state. The UNKNOWN super-state is designed for situations when the elderly carrier is at unknown locations (i.e. Situation 3 & 4). The MS performs AGPS-enabled tracking in the SATELLITE sub-state. This is the situation in which the previous AGPS fix has been successful, indicating that the tracker is in a location where GPS signals are available. Here the MS schedules AGPS fixes at an interval of DG. In the CELL sub-state, the previous AGPS fix attempt has been unsuccessful. That is, an AGPS fix cannot be computed within the timeout interval TG allowed. This indicates that the MS is likely to be at locations where GPS signals are not available, and hence AGPS fixes are avoided and the MS would simply report its cell ID and the server will perform cellular positioning. In Fig. 2, the set Observed Cell ID (C) is the set of cell IDs observed in the data collection period of an elderly carrier. C is kept only at the server. The set Common Cell ID (CCID) is the set of cell IDs associated with all the recognized PCLs of an elderly carrier. The PCL recognition algorithm is to be described in the next section. A copy of the CCID set,
Fig. 2 State Transition Diagram Table II specifies the actions (Ai) and the events (Ei) that lead to the various transitions in the state transition diagram, and Table III summarizes these actions and events. TABLE II STATE TRANSITION TABLE IN-PCL
PCL2PCL
COMMON
SATELLITE
CELL
UNKNOWN
IN-PCL
----
A1/E1
----
----
----
----
PCL2PCL
A1/E3
----
----
A2/E5
----
----
COMMON
----
----
----
A1/E2
----
----
SATELLITE
----
----
----
----
A3/E6
----
CELL
----
----
----
A1/E7
----
----
UNKNOWN
A1/E3
A1/E4
----
----
----
----
Next Current
TABLE III SUMMARY OF ACTIONS AND TRIGGER EVENTS Action/Event
Description
A1
Detect the current cell ID
A2
Report the current cell ID to the server
A3
Start an AGPS fix
E1
The current cell ID is in C, but not in CCID
E2
The current cell ID is not in C
E3
The current cell ID is in CCID
E4
MS receives a message of successful PCL prediction
E5
MS receives a message of failed PCL prediction
E6
AGPS fix attempt is not completed within interval TG
E7
Cell ID change is detected, new cell ID is not in C
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
III.
OFF-LINE PCL RECOGNITION
Before applying our on-line tracking algorithm, we need to recognize the PCLs of an elderly carrier from some historic cell ID data and represent the PCLs using cell ID clusters. In our current implementation, the historic data is collected only once, followed by an off-line PCL recognition process which digests the collected data to create the two cell ID sets C and CCID and to cluster cell IDs in CCID into PCLs. Ashbrook and Starner [8] have proposed an algorithm for learning important locations using historic GPS-based location information. Important locations are learned based on the length of the time gap in the sequence of historic records. We employ a similar time gap measurement. Our off-line process contains two phases: data collection, and data pre-processing and recognition. A. Data Collection In this phase, the elderly carrier is requested to carry the MS for at least several days. The MS’s cell ID is observed and recorded with a time stamp at short intervals (e.g. 10 seconds) continuously. A null cell ID value is recorded if the cellular network signals are too weak for the cell ID to be detected. We assume also that there are no undetected cell ID changes during a detection interval. The cell ID record is downloaded from the MS at the end of the collection period for processing. B. Data Pre-processing and PCL Recognition With the assumption of no undetected cell ID changes, we calculate the total time the MS spends in each observed cell ID throughout the data collection period as follows: te
T j = ∫α j ( t ) dt
(1)
ts
where αj(t) is an indicator function that is equal to 1 if the last cell ID recorded immediately before time t is CIDj; it is equal to 0 otherwise. ts and te are the starting and ending times of the collection period. Mika and Raento [6] use an exponential weighting function of time in their algorithm for important location recognition in order to give more weight to locations that are visited recently. We do not apply a weighting function in (1) because for now we collect the historic data only once. In the future, we may enhance our algorithm to collect cell ID data continuously while the MS is in the on-line phase. In that case, use of a weighting function can be considered. Next, we proceed to the PCL recognition phase. Initially the CCID set is empty. We deploy a time proportion threshold threT (0 < threT < 1) so that CIDj is put into the CCID set if the time spent in it is above the threshold: CCID = {CID j : T j > threT ⋅ Tcol }
(2)
The choice of threT is an important design parameter. If threT is too small, some cell IDs may be mistakenly included in the CCID set; if threT is too large, we may end up with only a few cell IDs belonging to a single PCL where the elderly carrier spends most of his/her time. For now threT is chosen empirically and some experimental results are shown in section VI. In a cellular network, network cells are often overlapping, and the serving cell of the same location may oscillate among
several alternatives. Therefore, it is unlikely that a PCL will be represented by only one cell ID, and it is desirable to cluster the cell IDs in the CCID set to avoid false transitions from PCLs. Ashbrook and Starner [8] proposed a variant of the Kmeans clustering algorithm to cluster GPS location measurements. This could not be applied in our clustering problem because the location of a network cell cannot be explicitly implied from its cell ID. Mika and Raento [7] described an unsupervised algorithm that clusters neighboring cells by testing cell ID oscillations. We do not adopt this algorithm because PCLs of an elder may be very close to each other, and cell IDs representing different PCLs may switch directly from one to the other. Such transitions should not be treated as cell ID oscillations. We propose to use a supervised clustering approach based on a self-report from the elder, with the help of others if necessary. The elder is asked to associate a location label (home, restaurant, elderly center, etc.) to the time instances when particular cell IDs in CCID are detected. This way, contextually meaningful location labels can be created, and cell IDs with the same labels are clustered into a single PCL. IV.
ON-LINE DESTINATION PCL PREDICTION
The final aspect of our algorithm is the on-line destination PCL prediction process which seeks to further reduce the number of AGPS fixes by keeping the MS in the COMMON super-state even when the elderly carrier is on the move. Bhattacharya and Das [5] proposed a cellular network path learning algorithm based on Markov chains. Their algorithm predicts the next cell in the path. We employ a similar probabilistic model, but we predict the destination PCL instead of only the next cell. A cell ID transition sequence of length L is represented as S L = {S1 , S 2 " S L }
(3)
where S1, S2 … SL is a sequence of successive distinct cell IDs. We define cell ID route as a cell ID transition sequence of at least 3 cell IDs in which the first and the last cell ID belong to the CCID set, but all the cell IDs in-between do not belong to the CCID set. The historic data contains a set of cell ID routes. We use Mij to denote the total number of cell ID routes from PCLi to PCLj found in the historical record and Rij(S) to denote the number of these routes which contains the cell ID transition sequence S at least once. We implement a Bayesian classifier with the maximum a posterior (MAP) decision rule in the server to predict the destination PCL when the elder is on the move. Specifically, the input to the classifier contains the starting PCL and a detected cell ID transition sequence whose first cell ID may or may not be in the CCID set and the last cell ID is not in the CCID set. Assume that PCLi is the starting PCL and SL is the detected transition sequence, the predicted destination PCL, PCLD, is:
{
}
PCLD = arg max P ( PCL j | PCLi ) ⋅ P ( S L |PCLi , PCL j ) PCL j
(4)
where P(PCLj|PCLi) is the prior probability of PCLj as a destination and P(SL|PCLi, PCLj) is the likelihood of SL. These probabilities can be computed using (5) and (6):
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
⎧ 0 ⎪ M ⎪ P ( PCL j | PCLi ) = ⎨ N ij ⎪ M ik ⎪⎩ ∑ k =1
if M ij = 0 otherwise
if M ij = 0 ⎧ 0 ⎪ P ( S L |PCLi , PCL j ) = ⎨ Rij ( S L ) otherwise ⎪ M ij ⎩
V.
(5)
(6)
VI.
It turns out that to implement the new tracking algorithm on the commercialized tracker we deployed in [1] would involve a large cost. Therefore, in the current research, we have adopted the strategy of first implementing the tracking algorithm on a GPS-enabled off-the-shelf mobile phone using a Python program. This strategy allows us to quickly and inexpensively conduct experiments on different aspects of our algorithm. We have further adopted the strategy of first evaluating the energy efficiency of our algorithm against an assumed energy consumption model. We avoided measuring actual MS battery consumption because with the Python implementation, the cellular phone could not enter the standby mode, leading to an energy consumption characteristic that differs significantly from is expected in an actual system. We made also the following simplifications: 1) the average power for fix calculation (PG) of the AGPS receiver is constant, 2) the energy consumed (ECOMM) by each cellular communication session (either GPS assistance data retrieval or location information report) is fixed, and 3) the time required by each communication session is negligible. The total energy used, E, by the MS over a period, can be represented as a sum of three components: (7) E = ES + EG + EC where ES is the energy for MS standby, EG for GPS calculation, and EC for cellular communication. With PS being the standby power of the MS, the three components can be computed as:
⎛ NSG ⎞ EG = PG ⋅ ⎜ ∑TTFFi + NUG ⋅ TG ⎟ ⎠ ⎝ i =1
A. Data Collection Three non-elderly volunteer carriers – Carrier A, B and C – serves as our initial experimental objects. Cell ID records are collected from them for 5 days continuously (i.e. 120 hours) with a detection interval of 10 seconds. During the data collection period, the carriers followed regular life patterns that mainly consist of moving between their homes and work places. Table IV summarizes the historic cell ID data. TABLE IV SUMMARY OF CELL ID DATA Collection Cell ID Observed Carrier Period Records Cell ID A 120 hours 42,770 18 B 120 hours 42,967 43 C 120 hours 42,842 350 The number of observed cell IDs of Carrier C is much larger than those of A and B because Carrier C traveled a much longer distance from his home to his work place. B. Off-line PCL Recognition For our off-line PCL recognition, we need to specify the value of the time proportion threshold threT. Fig. 3 shows the effect of threT on the CCID sets of the three carriers.
(8)
(9)
(10) EC = ( N A + N R ) ⋅ ECOMM where T is the total duration of the tracking period, NSG the number of successful AGPS fixes, NUG the number of unsuccessful AGPS fixes, NA the number of GPS assistance data retrievals, NR the number of location reporting, TG the maximum time allowed for an AGPS fix, and lastly TTFFi the Time to First Fix [9] of individual fixes. The average operating power (Pavg) of can be computed as ES + EG + EC (11) T By varying the fix interval and measuring the time taken to deplete the MS battery by a fixed amount, we can create 3 or more sample points that allow us to estimate (with regression if we have more than 3 points) the three parameters PS, PG and Pavg =
EXPERIMENTS AND EVALUATION
The algorithm is implemented on the MS through a Python program and in the server through Java.
ENERGY CONSUMPTION
N SG ⎛ ⎞ ES = PS ⋅ ⎜ T − ∑TTFFi − NUG ⋅ TG ⎟ i =1 ⎝ ⎠
ECOMM. Then by using (11), we can evaluate the energy efficiency of our tracking algorithm by comparing its average operating power to that of other tracking approaches. We can also evaluate the energy efficiency when trackers with different energy consumption parameters are used.
Fig. 3. Effect of threT on the CCID Set We can see that the number of elements in the CCID sets of the three carriers decreases at almost the same rate as threT increases. With a time proportion threshold of 0.04 (i.e. 1 hour in 24 hours), two PCLs (i.e. homes and offices) have been successfully recognized for Carrier B and Carrier C, while three PCLs have been recognized for Carrier A. Table V shows the supervised recognition results of Carrier A, whose activity region is mainly within the university campus. TABLE V RECOGNITION RESULTS OF CARRIER A PCL ID PCL1
Cell ID Cluster {2163795, 2172440}
PCL Labels Home@Tower
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PCL2 PCL3
{1101, 1102, 31102} {2163789, 2173790}
Office@3118A Restaurant@LG1
C. On-line Tracking Experiments For on-line tracking, the three carriers were told to follow similar activity patterns as those in the data collection period. Each of the experiments lasted for 24 hours continuously (i.e. from 12am in Day 1 to 12am in Day 2). Fig. 4 shows the time distribution of different sub-states for the different carriers. The MS spent most of the time (e.g. 100% for Carrier A and Carrier B, 92% for Carrier C) in the COMMON super-state. For Carrier A and Carrier B, their MS’s have not triggered any AGPS fix since they have spent all the time in the COMMON super-state. Carrier C’s MS spent some time working in the UNKNOWN super-state because the carrier traveled a much longer distance from home to work, making the possibility of encountering unknown cell IDs much larger.
Fig. 5 shows the average operating power of the MS’s over different three-hour intervals in the experiments. The average operating power is calculated based on (11). All the power values are normalized by the standby power of the cellular phone. We compare the average operating power under our algorithm with the standby power of the MS as well as the average power of a MS deploying a cell ID positioning approach where cell ID is reported every minute. Fig. 4 shows that our algorithm always leads to a lower average operating power than the cell ID positioning approach. Sometimes the MS with our algorithm can even achieve an average operating power close to the cellular phone’s standby power. VII. CONCLUSIONS In this paper, we describe an energy-efficient elderly tracking algorithm based on the concept of Personal Common Locations (PCL). As described in the state transition diagram, we use a simple two-state model to model the lifestyle of an elderly carrier. Through experiments with the help of three carriers, we have shown that the MS with our algorithm spends most of the time performing energy-saving tracking if the carriers follow common activity patterns. The number of necessary AGPS fixes in the tracking process is significantly reduced, resulting in a noticeable increase in battery lifetime. VIII. ACKNOWLEDGMENT
Fig. 4. Time Distribution of Sub-states When a carrier has stayed in a PCL for a long time, occasionally cell IDs that are not in the CCID set may be detected. These cell IDs might have been observed in the data collection period, but were not put in the CCID set because they were only detected from time to time. When these cell IDs are detected, the MS enters the PCL2PCL sub-state according to our state transition diagram. In the PCL2PCL sub-state, the MS reports the cell ID to the tracking server for destination PCL prediction. Therefore, the time spent in the PCL2PCL sub-state may be larger than the actual time that the carrier spends moving between PCLs. However, this does not significantly affect the performance of our tracking algorithm because the PCL2PCL sub-state is still energy-efficient (e.g. AGPS is disabled) and the predicted PCL is still the actual PCL in which the user is staying.
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Fig. 5. Comparison of Average Operating Power
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