Enhancing the Sustainability of A Location-Aware Service through Optimization

Abstract

A location-aware service (LAS) is an imperative topic in ambient intelligence; an LAS recommends suitable utilities to a user based on the user’s location and context. However, current LASs have several problems, and most of these services do not last. This study proposes an optimization-based approach for enhancing the sustainability of an LAS. In this paper, problems related to optimizing an LAS system are presented. The distinct nature of an LAS optimization problem in comparison with traditional optimization problems is subsequently described. Existing methods applicable to solving an LAS optimization problem are also reviewed. The advantages and disadvantages of each method are then discussed as a motive for combining multiple optimization methods in this study, as illustrated by an example. Finally, opportunities and challenges faced by researchers in this field are presented.

Full text

Sustainability 2014, 6, 9441-9455; doi:10.3390/su6129441
sustainability
ISSN 2071-1050
www.mdpi.com/journal/sustainability
Article
Enhancing the Sustainability of a Location-Aware Service
through Optimization
Horng-Ren Tsai 1 and Toly Chen 2,*
1 Department of Information Technology, Lingtung University, No. 1, Lingtung Rd,
Taichung City 408, Taiwan; E-Mail: hrt@teamail.ltu.edu.tw
2 Department of Industrial Engineering and Systems Management, Feng Chia University, No. 100,
Wenhua Rd, Taichung City 407, Taiwan
* Author to whom correspondence should be addressed; E-Mail: tolychen@ms37.hinet.net;
Tel.: +886-4-2451-7250 (ext. 3645); Fax: +886-4-2451-0240.
External Editor: Marc A. Rosen
Received: 26 August 2014; in revised form: 9 December 2014 / Accepted: 15 December 2014 /
Published: 18 December 2014
Abstract: A location-aware service (LAS) is an imperative topic in ambient intelligence;
an LAS recommends suitable utilities to a user based on the user’s location and context.
However, current LASs have several problems, and most of these services do not last.
This study proposes an optimization-based approach for enhancing the sustainability of an
LAS. In this paper, problems related to optimizing an LAS system are presented.
The distinct nature of an LAS optimization problem in comparison with traditional
optimization problems is subsequently described. Existing methods applicable to solving an
LAS optimization problem are also reviewed. The advantages and disadvantages of each
method are then discussed as a motive for combining multiple optimization methods in this
study, as illustrated by an example. Finally, opportunities and challenges faced by
researchers in this field are presented.
Keywords: location-aware service (LAS); optimization; sustainability; ambient intelligence
OPEN ACCESS

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1. Introduction
A location-aware service (LAS), or location-based service, is a widely discussed topic in ambient
intelligence [1–3]. An LAS is a special context-aware service that recommends suitable utilities to a user
based on the user’s location [4]. The main research fields of LAS include mobile commerce,
human-computer interfaces, remote detection, and ubiquitous computing. Dialing an emergency number
using a cell phone, package tracking systems, navigation systems, and mobile marketing are typical
examples of LASs.
Park et al. [5] reported that providing LASs is a challenging task because of latency, limited display,
and intermittent connectivity to the backend database. Nevertheless, according to Zickuhr [6], as of
2012, approximately three-quarters of smartphone owners have used LASs. Raper et al. [7] listed three
emerging areas of LASs as location-based gaming, assistive technology, and location-based health
applications. Kruger et al. [8] reported that location determining and situational responsiveness are
particularly crucial to mobile guides, but are still far from perfect. Espeter and Raubal [9] recently
considered personalization as one of the most vital developments of LASs; however, how to support
multiple users simultaneously remains to be studied [10].
In summary, existing LASs demonstrate the following problems:
(1) There is no systematic procedure for designing a practicable LAS.
(2) Most LAS applications have not included cost-benefit analyses [11]. One reason is that massive
government support is not focused on making profit. Another reason is the difficulty in collecting
the relevant information on the client/user side. In addition, relating the final action of a user to
the LAS provided is a difficult task. However, to ensure the sustainability of an LAS, these
problems must be overcome to conduct a credible cost-benefit analysis.
(3) Most LASs are not always lasting; therefore, ongoing developments of new LASs may not
be worthwhile.
(4) Most LAS applications can be modeled as human-system interaction processes of which human
factors/ergonomics are an indispensable part and should be greatly emphasized.
An LAS system can resolve these problems and pursue sustainable development in the following
manners (Figure 1): continuously updating the database, adding new features, and retiring uninteresting
services; providing more options and flexibility; and improving the suitability for use. This involves
multiple facets, and is a process that relies heavily on users’ feedback and must evolve over time.
In addition, according to Problem 3, this process can be considered a long-term optimization process,
and some small-scale short-term optimization actions are undertaken at each time point for improving
the LAS system. However, because enabling an LAS system to operate smoothly is a highly difficult
task, most LAS systems have not been optimized. One possible reason is that some LAS systems must
serve many people simultaneously [9]. The problem of optimizing LAS performance for multiple users
is extremely complicated, even in the short term. In addition, the goal of an LAS is to meet the needs of
users that are diverse and change over time; therefore, they cannot be fully quantified.

Sustainability 2014, 6 9443
Figure 1. A sustainable Location-aware service (LAS).

Sustainable LAS
Adding new
features
Providing more
options
Continuously
updating the
databases
Improving the
suitability for
use
Retiring
uninteresting old
services
Providing more
flexibility
New ways of
profitability
IT
Product Customer
/Service Relationship
time
The objective of this study was to discuss problems related to optimizing an LAS system. First, the
distinct characteristics of an LAS optimization problem compared with traditional optimization
problems is described. The objectives and constraints faced when operating an LAS system are
subsequently summarized into several categories. The solution space for the LAS optimization problem
can be established based on these items. Subsequently, existing methods applicable to solving an LAS
optimization problem are reviewed. The advantages and disadvantages of each method are also
discussed, which motivated the combination of multiple optimization methods, as illustrated by an
example. Finally, opportunities and challenges faced by researchers in this field are presented.
2. Distinct Characteristics of an LAS Optimization Problem
Optimizing an LAS is a controversial problem. Numerous LASs involve human decision-making
processes, such as deciding whether to follow the results of an online restaurant recommendation
system. However, human decision-making is not strictly optimizing in an economical and mathematical
sense [12,13]. In addition, representing people’s subjective feelings by using a simple scale, as
performed in several other fields, is inappropriate [14]. Therefore, an LAS optimization problem cannot
be resolved simply by applying heuristics.
Optimizing an LAS is also a difficult task. First, bulk information may need to be processed,
which renders the optimization model extremely large. In addition, such data are dynamic and often
incomplete [15], and this phenomenon poses a challenge to the adaptability and robustness of the
optimization model. Furthermore, users’ preferences for the recommended service are unclear, vague,
inconsistent, and difficult to quantify. Setting a single objective function that is applicable to everyone is

Sustainability 2014, 6 9444
thus a difficult task [9,16,17]. In addition, cultural differences also considerably influence optimizing an
LAS. For example, dietary preferences are crucial inputs to a restaurant recommendation LAS.
However, dietary preferences are typically culturally specified and can convey different meanings in
different social or cultural settings. This implies that the relationship among the variables in the
optimization model may differ according to culture. Furthermore, data incompleteness is another
problem. In most cases, users are unwilling or find it inconvenient to answer all the questions, for
example, when in need of a distant emergency care. However, the system must still assist the user by
making decisions based on incomplete information.
3. LAS Optimization Problem
3.1. Objectives and Constraints
Most LAS systems are configured as client-server systems [18]. The objectives and constraints on the
client side, server side, or for the whole LAS system must be considered. On the client side, typical
objectives include the average time for filling in a request, average time for receiving a response,
average service level [15], average waiting time for the requested service, traversal time [11],
suitability [16,19], recall rate, precision rate, and F1 metric. On the server side, the objectives include the
required investment, number of app downloads, number of requested services, commission received,
return on investment, and payback period. For the whole LAS system, the objectives include the number
of successful recommendations and amount of purchases through the system. Essentially, the
performance on the client side influences that on the server side. The performance of the whole LAS
system depends on both sides (Figure 2).
Figure 2. Relationships among the objectives.
Performance on
the client side
Performance on
the server side
Performance of
the LAS system
Rinner and Raubal [20] reported three spatiotemporal constraints on an LAS:
(1) Capability constraints: The location that can be reached by a user is limited by the user’s
transportation mode.
(2) Coupling constraints: In a group LAS, the location that may be reached by a user must be
reachable to the other users.
(3) Authority constraints (i.e., the accessibility of a service location at different time intervals): The
needs and preferences of a user are objectives of as well as constraints on an LAS system.

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3.2. Solution Space
In most LASs, only a few (or countable) alternatives are available, thus forming a discrete feasible
region with limited solutions. For example, in a restaurant recommendation LAS, the number of
restaurants that can be reached by a user within a certain period of time and are in accordance with the
user’s needs is only 10. However, these alternatives can be practiced using an infinite number of
methods (i.e., infinite possible actions). In addition, the user can go to the recommended restaurant at
different speeds via different paths. An optimization model can address either of the two problems, or
both problems simultaneously. For example, the restaurant recommendation LAS can recommend a
restaurant from several alternatives to a user, and subsequently determine the shortest path to the
restaurant (i.e., transportation planning in advance; TPI) (Figure 3). In this approach, the traversal time
to each restaurant is not considered in sorting the alternatives. Another method involves evaluating the
shortest path to each restaurant, and then incorporating the evaluation results in making the
recommendation (i.e., transportation planning afterwards; TPA) (Figure 4).
Figure 3. Transportation planning in advance.
Establish the shortest
path to each restaurant
Sort the alternatives
according to the pre-
specified criteria
Make the
recommendation
the shortest path
user requirements
ambient data
pre-specified
criteria
restaurant
the shortest path
Figure 4. Transportation planning afterwards.
Establish the shortest
path to the
recommended
restaurant
Sort the alternatives
according to the pre-
specified criteria
Make the
recommendation
restaurant
the shortest path
user requirements
ambient data
pre-specified
criteria

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Methods that are suitable for problems comprising discrete feasible regions include the ordered
weighted average (OWA) operator [20,21] and decision rules.
Another problem involves the similarity between two solutions. Some of the solutions also contain
other solutions. However, these phenomena enable a flexible recommendation; nevertheless, they
increase the complexity of decision making.
4. Applicable Methods for Solving an LAS Optimization Problem
Decision rules have been widely used for solving a location recommendation problem. For example,
Mateo et al. [22] established a restaurant recommendation system that involves using fuzzy inference
rules to process context information. Astrain et al. [23] established a fuzzy inference system for
estimating the location of a user. The inputs to the fuzzy inference system were Wi-Fi signal strengths in
specific zones, which were expressed in linguistic terms. The output from the fuzzy inference system
associated the user with a specific zone. Savage et al. [24] designed a restaurant recommendation LAS
system that processed a user’s location, preferences, feelings, and transportation mode by using decision
trees to make a recommendation. Decision rules are not self-optimizing, but rely on a systematic
procedure for improving their performance. If decision rules are established/selected subjectively, then
they should be adjusted if the outcome deviates from the reasoning result, or appended if they cannot be
applied to a new case. Furthermore, decision rules should be reorganized periodically according to the
most recent statistics to optimize their performance. One drawback of decision trees is that, occasionally,
the existing rules cannot cover all cases if they were subjectively established/chosen. A systematic
approach for establishing rules, such as using classification and regression trees, can be applied to solve
this problem. Another problem involves the misclassification of cases, which was resolved by
Savage et al. [24] by using a discrete hidden Markov model.
The second common practice involves optimizing (or improving) a prespecified criterion iteratively
with experts’ intervention. For example, Andrienko et al. [25] constructed a self-organizing map (SOM)
for analyzing spatiotemporal patterns. The clustering results were interpreted by experts. The SOM was
modified when outliers were observed. The process continues until only a few outliers remain.
The OWA operator is another technique often applied to multicriteria LAS optimization processes
because of its ease of use. For example, Rinner and Raubal [20] designed a hotel recommendation service
called Hotel Finder that recommends hotels to a user by considering the user’s location, spatiotemporal
constraints, and preferences. The performance of a restaurant in different aspects were aggregated
through the OWA operator. However, for optimizing the performance, the design strategy of the OWA
operator must be adjusted continuously. Another drawback of the OWA operator is that the utilities of
some alternatives may be the same (i.e., ties). For overcoming this problem, several advanced OWA
operators—such as the basic defuzzification distribution (BADD) OWA operator [21], additive neat
BADD OWA operator [26], intuitionistic OWA operator [27], and most preferred OWA (MP-OWA)
operator [28]—have been proposed in recent years. Table 1 shows a summary of these rules.

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Table 1. Various ordered weighted averages (OWA) operators.
Operator Formula Variable Meanings
OWA
(1) Sort {| 1~}
ij
x
jK along j to obtain
,1
{| 1~, }
ij ij i j
yj Ky y


 for each i
(2)
1
n
ijij
j
Uwy


(3) Choose alternative i that maximizes i
U
 i: alternative, i = 1 ~ N
 j: criterion, j = 1 ~ K
 j
w: weight of the j-th position,
(,,)
j
wfNj


 ij
x
: performance of alternative i
in attribute j
 i
U: utility of alternative i
BADD-OWA (1)
1
n
iijij
j
Uwy

; (2)
1
/
K
ij ij ij
j
wy y





additive neat OWA (1)
1
n
iijij
j
Uwy

; (2)
1
()/ ()
K
ij ij ij
j
wfy fy


MP-OWA
(1) {| 1~}
l
SSl L is a scale containing L
values to choose
(2) **
1
max( )/ max( )
K
j
jl jl
ll
j
wNxS NxS

 

As mentioned previously, data incompleteness is an unavoidable problem that hinders the operation
of an LAS. One method for resolving this problem involves considering only the dimensions with
complete data. Ties are broken by considering the remaining dimensions without complete data. Some
OWA operators can also address data incompleteness. For example, Herrera-Viedma et al. [29]
proposed an iterative procedure for estimating the missing information in an expert’s incomplete fuzzy
preferences. The additive consistency OWA operator was subsequently proposed for sorting alternatives.
Mathematical programming is a traditional optimization method that has been applied to LAS
optimization problems. Chen and Wu [30] formulated a problem of determining the just-in-time service
location for a user as an integer-nonlinear programming (INLP) problem. Lin and Chen [11] also solved
a biobjective fuzzy INLP (FINLP) problem to recommend a user a path for maximizing the timeliness of
reaching a service location and minimizing the time remaining to reach the destination. However, the
practical applicability of mathematical programming is limited because implementing this technique
online is a difficult task.
In the literature, heuristics have been proposed for overcoming the difficulty associated with
mathematical programming. Heuristics are more easily programmed, and can determine a near-optimal
solution. For example, Chen and Huang [16] proposed a fuzzy Dijkstra algorithm for determining the
just-in-time output location in a ubiquitous printing system. To further enhance the efficiency of solving
the FINLP problem proposed by Lin and Chen [11], Chen [10] established a parallel processing scheme
that considered multiple service locations simultaneously and solved the problem backward.
Applications of soft computing techniques are also observed in this field. By positioning a user
indoors, Link et al. [17] matched the detected steps of the user onto the expected route by using sequence
alignment algorithms that involve arranging sequences to identify regions of similarity in
Bioinformatics, similar to the concept of dynamic programming. However, soft computing methods may
be excessively complicated and time-consuming to be suitable for online applications.
Most LAS systems must serve many users simultaneously. One approach involves addressing these
requests individually, which is more convenient in practice; however, resources can be allocated more

Sustainability 2014, 6 9448
effectively by considering multiple needs simultaneously to provide users with high-quality services. In
addition, some users may belong to the same group; therefore, their needs can be met collectively. By
contrast, Espeter and Raubal argued that instead of pursuing an optimal overall performance, focus
should be placed on not sacrificing the utility of any user.
Figure 5 illustrates a summary of applicable methods in this field. Table 2 shows a summary of the
advantages and disadvantages of these methods, which motivated the combination of multiple
optimization methods in this study.
Figure 5. Methods applicable to a location-aware service (LAS) optimization problem.
LAS optimization
problem
Mathematic
programming
models
Heuristics
Decision rules Fuzzy inference
rules/systems
OWA
Soft computing
Sustainability of a
LAS
Table 2. Advantages and disadvantages of various optimization methods.
Optimization
Method Advantages Disadvantages
Decision
Rules
 Easy to implement
 Efficient
 Need to be updated continuously
 Often non-optimal
Interactive
Methods
 Easy to communicate
 Flexible  Often non-optimal
OWA  Consider the quality performance
 Easy to implement
 Subjective
 Often non-optimal
Mathematical
Programming  Usually optimal  Can consider only small-scale problems
 Not compatible with other modules
Heuristics
 Easy to program
 Easy to implement
 Efficient
 Solutions may be far from optimal
 Difficult to be adapted to new situations
Soft
Computing
 Optimal or near-optimal solution
 Compatible with other modules
 Complicate
 May be time-consuming

Sustainability 2014, 6 9449
5. Example of Combining Different Optimization Methods
Figure 6 shows an example in which a user travels from S (the current location) to D (destination).
On the way to the destination, the user requests a service from one of 10 possible service locations
(indicated by A–J, respectively) and requires a recommendation from the LAS system. Table 3 shows a
summary of the user’s requirements and conditions of the service locations.
Figure 6. Example of combining different optimization methods.
S D
1
A
H
D
2
G
4
E
3
F
J
I
5
C
B
3
0.8
0.8
2
1
13
2
4
2
0.7
3
3
2
2
1.3
Table 3. Requirements of the user and conditions of the service locations.
Entity Service Fee (NTD) Estimated Waiting
Time (min) Decoration Space
User ≤1500 ≤30 Acceptable Spacious
A 2300 20 Luxurious Spacious
B 1200 50 Acceptable Moderate
C 1780 25 Acceptable Spacious
D 550 90 Acceptable Narrow
E 1800 40 Luxurious Narrow
F 1320 65 Acceptable Moderate
G 750 20 Acceptable Narrow
H 3500 30 Luxurious Spacious
I 660 75 Acceptable Spacious
J 1100 85 Luxurious Spacious
The objective function is used to maximize the average suitability S:
Max S (1)
Based on the user’s requirements, the formulae for evaluating the suitability are as follows:

Sustainability 2014, 6 9450
(Service Fee)
1
1 service fee 1500
2000 service fee 1500 service fee 2000
500
0
if
Sif
otherwise









(2)
(Estimated Waiting Time)
2
1 estimated waiting time 30
60 estimated waiting time 30 estimated waiting time 60
30
0
if
Sif
otherwise









(3)
(Decoration)
3
1 decoration "acceptable" "luxurious"
0
if or
Sotherwise



 (4)
(Space)
4
1 space "spacious"
0.5 space "moderate"
0
if
Sif
otherwise







(5)
The suitability of the traversal time can also be evaluated as follows:
(Traversal Time)
5
traversal time
1 traversal time 30
30
0
if
S
otherwise





(6)
where the upper time limit, 30 min, is established by considering the target of the estimated waiting time
in (3).
To apply the TPI method, the shortest path to each service location is determined by solving the
following INLP problem:
Min n
d (7)
,1~; ;
i j ji ji
ddli njil    (8)
,
(),1~
ji
ijijji
jil
dxdlin


 (9)
,
1, 1 ~
ji
ji
jil
x
in


 (10)
{0,1}, 1 ~ ; ;
ji ji
xinjil  (11)
where n is the number of nodes in the traffic network. The length of the path connecting nodes i and j is
lij; i, j = 1 ~ n; i  j; lij =  if there is no connection between the two nodes. If nodes are numbered from

Sustainability 2014, 6 9451
the start point to the destination, then the start point and destination are nodes 1 and n, respectively. In
addition, no back path is allowed (i.e., lij =  if i > j). The shortest distance from the start point to node i
is represented by di. Obviously, d1 = 0 and max
ni
i
dd

. Table 4 shows the results.
Table 4. Shortest path to each service location.
Service Location Shortest Path Traversal Time (min)
A S->A 0.8
B S->1->2->B 4.5
C S->A->4->C 4.8
D S->1->2->D 4.8
E S->1->2->D->E 5.8
F S->1->2->D->E->F 8.8
G S->1->3->G 9
H S->1->2->B->H 5.2
I S->1->2->B->H->I 8.2
J S->A->4->C->5->J 8.8
The average satisfaction level of every service location along each dimension is subsequently
evaluated, and Table 5 shows a summary of the results. Service location C obtains the highest average
satisfaction level. By contrast, if the TPA method is applied, the traversal time is not considered when
reasoning, and the most suitable service location is G. The shortest path to G is S->1->3->G.
Table 5. Satisfaction level of each service location.
Service Location S1 S
2 S
3 S
4 S
5 S (TPI) S (TPA)
A 0 1 1 1 0.97 0.79 0.75
B 1 0.33 1 0.5 0.85 0.74 0.71
C 0.44 1 1 1 0.84 0.86 0.86
D 1 0 1 0 0.84 0.57 0.50
E 0.4 0.67 1 0 0.81 0.57 0.52
F 1 0 1 0.5 0.71 0.64 0.63
G 1 1 1 0.5 0.70 0.84 0.88
H 0 1 1 1 0.83 0.77 0.75
I 1 0 1 1 0.73 0.75 0.75
J 1 0 1 1 0.71 0.74 0.75
For applying the OWA operator, the satisfaction levels along the five (or four) dimensions are sorted,
as shown in Table 6, in which S(i) indicates the i-th highest satisfaction level, i = 1–5 (or 4). The
“moderately optimistic” decision strategy is then adopted. The ordered weighted average satisfaction
level of each service location is calculated, and Table 6 also shows the results. As shown in Table 6, the
optimal service location obtained using the TPI method is C, whereas that obtained using the TPA
method is A. The results are slightly different from those obtained when the average satisfaction level is
maximized. This is a property vital to the flexibility of an LAS.

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Table 6. The sorted satisfaction levels.
Service Location S(1) S
(2) S
(3) S
(4) S
(5) OWA (TPI) OWA (TPA)
A 1 1 1 0.97 0 0.93 1.00
B 1 1 0.85 0.5 0.33 0.90 0.94
C 1 1 1 0.84 0.44 0.95 0.99
D 1 1 0.84 0 0 0.84 0.90
E 1 0.81 0.67 0.4 0 0.83 0.89
F 1 1 0.71 0.5 0 0.87 0.93
G 1 1 1 0.70 0.5 0.94 0.98
H 1 1 1 0.83 0 0.92 0.99
I 1 1 1 0.73 0 0.91 0.98
J 1 1 1 0.71 0 0.91 0.98
6. Opportunities and Challenges
As mentioned previously, combining multiple optimization methods facilitates the process of
improving the effectiveness and flexibility of an LAS system, and should be studied further in the future.
Improving the precision of positioning a user is another critical problem in the sustainability of an
LAS [31]. Several attempts have recently been made in this regard. Some advanced technologies have
been proposed for indoor user positioning, as summarized in Table 7. Regarding outdoor user positioning,
Japanese engineers are currently initiating the first commercial, nationwide, centimeter-scale satellite
positioning technology by precisely correcting GPS signal errors [32]. Once successful, user positioning
is expected to be highly precise, thereby reducing the possibility of misleading a user. The popularity of
wearable devices also presents opportunities; for example, an LAS system can “see” rather than “guess”
the location of a user through Google Glass worn by the user.
Table 7. Advanced technologies for indoor user positioning.
Reference Method Description Results
Astrain
et al. [23]
Fuzzy inference
system
Inputs to the fuzzy inference system are Wi-Fi
signal strengths in specific zones; the output from
the fuzzy inference system is the membership
that a user is in a specific zone.
 a correct recognition
rate from 84% to 90%
Link
et al. [17]
Map-based
indoor
navigation
The detected steps of a user is mapped
onto the expected route using sequence
alignment algorithms.
 able to guide a user
turn-by-turn
Ruiz-Ruiz
et al. [33]
Vision-enhanced
multisensor LBS
A coarse-grained estimation is first obtained
based on WiFi signals, digital compasses, and
built-in accelerometers. Then, the position of a
user is determined using fingerprinting methods,
probabilistic techniques, and motion estimators.
 the positioning error
≤ 15 cm
 the response time
≤ 0.5 s
Sakamoto
et al. [34] Doppler IMES
The position and orientation of a user with a
receiver are estimated according to the Doppler
shifts produced by moving the receiver antenna
with two or more IMES transmitters.
 the positioning error
≤ a few decimeters
 the orientation error
≤ a few degrees

Sustainability 2014, 6 9453
LASs for special groups, such as the blind, vision impaired, and the elderly, have not yet been
designed. In this regard, Gallagher et al. [35] proposed several criteria—including positioning accuracy,
robustness, seamless integration with the environment, and the nature of information to be
provided—for evaluating the success of an LAS for such groups.
Several indoor LAS systems guide a user to the location containing commodities in which the user is
interested. Popular and unpopular routes can be discriminated after collecting the data of a sufficient
number of users. Based on the results, the showcases of commodities can be rearranged, for example, to
shorten the distance that a user must travel. The contribution (e.g., number of visits or purchases) by a
user can also be considered by minimizing the weighted sum of distances instead, in which the weight of
a route is equal to the average contribution of users that have traveled along the route.
Another trend to be expected is increased cooperation among different service locations through a
common LAS system. However, this is based on the premise that these service locations are willing to
cooperate and provide more operating information, such as availability and the average waiting time, to
the LAS system.
Acknowledgments
This work was partially supported by the Ministry of Science and Technology of Taiwan under
Grant No. MOST 103-2221-E-035-043-MY3.
Author Contributions
Prof. Chen proposed the methodology and wrote most of the content of this paper. Dr. Tsai is
responsible for the design of the experiment detailed in Section 5, as well as the analysis of the
experimental results. Dr. Tsai also helped revise the main text, and arranged the English editing service
of this paper.
Conflicts of Interest
The authors declare no conflict of interest.
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