1. Introduction
The transportation system especially in urban areas is required to be reevaluated
due to various factors like the increased number of vehicles on the road, parking
demand, and its impact on the environment. Along with this, the gap between public
and private transport is also one of the main reasons for the development of new mobility
trends like vehicle sharing. This mobility trend is known as Mobility as s Service
(MaaS). The main aim of the MaaS is to provide a smooth, integrated, diverse form
of the fully connected journey in a centralized fashion [1,2]. The motivation behind this MaaS is the exponential growth of the number of vehicles
on the roads, especially in metropolitan areas. MaaS is an old concept that gained
huge attention during the European oil crisis that hit Europe a decade ago, that urges
the need for shared mobility to compensate for the shortage of oil. This trend also
brings various pertinent benefits like reducing cost, less environmental footprints,
and of course less usage of oil. MaaS was initially adopted as a daily commuter, however
now it is expanding to various other forms like customized, long-distance, and occasional
trips. Besides the horizontal expansions, it's also undergone vertical expansion where
various degrees of formality and regularity on MaaS arrangement are possible now.
Recently, the increase in the number of vehicles, especially private vehicles, has
greatly impacted the demand for fuel, which ultimately caused the increase in fuel
price, and this directly impacts environmental pollution. To overcome such issues
and provide a high level of trust and safety to the user, the development of the mobile-based
carpooling system is now a hot area of research and development these days [3]. MaaS can provide secure vehicle-sharing for short daily trips within the cities
and occasionally long-distance trips. Short daily trips consist of a period of less
than a day and long-distance trips are comprised of trips of more than one day. The
MaaS is a fully connected platform including private (individuals), and rental companies
(organizations) to provide the required services to individuals or groups of individuals
based on the requirement. The MaaS has shown up as eco-friendly, less time-consuming,
and a helping hand to alleviate traffic congestion [5], which is a fully connected form of collaboration among the drivers and passengers
constrained to the shared routes and costs [5]. The most prominent aspect is real-time service, which interacts with multiple apps
and various websites to manage ad-hoc ride arrangements based on specific pickup points
or designated pick-up locations [6]. The MaaS are formally known as Carpooling, whereas another form of MaaS is known
as Vanpooling, which can accommodate a group of people, who share multiple destinations
with a shared route [7].
Together these all, are reshaping the urban mobility transportation system, which
can offer sustainable and efficient alternatives while addressing the problem of traditional
transport systems [8]. These services play a vital role in fulfilling the needs of those areas where conventional
transport is inefficient. This trend requires a clear understanding of shared mobility,
and the share economy, to meet and establish the future requirement. It also aids
the government in establishing clear and effective policies for shared mobility like
taxes, insurance, parking regulation, and support for its business model as shown
in Fig. 1 [9].
Fig. 1. Carsharing business models.
In the 1980s, Germany and Switzerland were the first countries to introduce car-sharing
as a business-to-consumer (BC) model, where various companies leased their vehicle
to clients based on round trip (RT) usage, where the vehicle had to return to its
initial point after usage. After three decades, in 2009, the one-way (OW) scheme was
introduced to facilitate the drop-off at specific locations. A peer-to-peer (P2P)
model was seen in 2011, where supplementary services are also provided by the car
owner like insurance. Recently the MaaS has shown its capability in a pandemic like
COVID-19 to meet the transportation requirement.
1.1. Survey Outline
This study underscores the pivotal role of carpooling in shaping the future of urban
mobility, offering safer alternatives to crowded transportation modes, and contributing
to environmental and energy conservation efforts. The organization and structure of
the survey paper are shown in Fig. 2. Introduction is given in Section 1, while Section 2 is comprised of the Background
of Carpooling. Section 3 explains Carpooling: an Overview, Section 4 explains the
core functionality of carpooling, Section 5 elaborates the Carpooling Systems: A Digital
Realm, Section 6 explains the various forms of carpooling, Section 7 explains the
availability aspects of carpooling, Section 8 explains the various challenges faced
by the carpooling, Section 9 highlights the existing literature, Section 10 explains
the role of the ICT in carpooling, benefits of carpooling systems are discussed in
Section 11, the conclusion is given in the Section 12, future direction is given in
the Section 13.
Fig. 2. Survey paper organization.
2. Background of Carpooling
Carpooling has experienced fluctuations in interest over time. Unlike the steady and
non-linear increase in the use of single-occupancy vehicles (SOVs) post-World War
II, carpooling garners attention during periods of favorable exogenous conditions,
such as fuel shortages or high fuel prices. Notably, the North American context has
witnessed three distinct phases of renewed interest in carpooling [10]. Carpooling first emerged on the government's radar during World War II when the
United States faced rubber and oil shortages [11]. However, after the war commuters returned to using SOVs, and carpooling seemed to
fade from prominence. The mid-1970s marked another phase of carpooling interest, driven
by oil supply concerns stemming from global instability and the Organization of Petroleum
Exporting Countries (OPEC) embargo on Western Europe and the United States. Yet, as
the crisis waned in the early 1980s, carpooling again receded, with a notable decline
in usage from nearly 20% to 13% between 1980 and 1990 [12]. In recent years, renewed interest in alternative modes of transportation has sparked
a resurgence in carpooling initiatives. Indicative of this trend is the increase in
the percentage of Canadian commuters traveling as passengers, rising from 6.9% to
7.7% between 1996 and 2006 [13]. The precise reasons behind this renewed enthusiasm for carpooling are multifaceted.
Environmental concerns, including carbon emissions and climate change, have played
a role.
Economic factors have also contributed to rising vehicle ownership and usage costs.
The economic downturn since 2008 may further boost interest as cost-effective travel
initiatives gain traction. Governments and other stakeholders are exploring applications
designed explicitly for carpooling to facilitate the process and foster connections
among potential riders. This governs an exponential increment in the market size of
carpooling as shown in Fig. 3, in which it is observed that in 2021 the market size was \$19.72 and it is expected
to reach up to \$79.36 by 2028, a marvelous increase in market size in a very short
period.
Fig. 3. Carpooling market size [11].
3. Carpooling: An Overview
Carpooling, a procedure, where two or more individuals share a car journey to a predetermined
destination, is essentially an organized form of hitchhiking facilitated by communication
systems connecting drivers offering rides with passengers seeking transportation [14]. A user equipped with a smartphone device can initiate the request of car sharing,
this request is served by the cloud having a database of all the available cars in
the vicinity of the user. The cloud database after finding a suitable car, has the
capacity for the required number of users is directed or the user is informed to reach
the meeting point. The car picks the user, moves to the next destination, drops the
user to his required destination, and keeps updating its status to the cloud database
for further car sharing. After completing the user tour, the car owner required the
server chargers, which can be executed in either cash offer or card payment as shown
in Fig. 4.
Fig. 4. Carpooling model from starting point to end point.
In principle, carpooling provides numerous benefits, including reducing traffic congestion,
and environmental pollution, and cost savings for drivers and passengers. The study
[15] emphasizes that carpooling is more effective than electric or hybrid vehicles in
reducing greenhouse gas emissions. However, despite its potential advantages, carpooling
has not been the primary mode of transportation for many, primarily due to concerns
regarding safety and trustworthiness. As the number of drivers continues to rise,
urban transportation infrastructures are increasingly unable to meet these escalating
demands, exacerbating traffic-related issues. It underscores the need for a secure
carpooling service to address these concerns and liability problems effectively. Notably,
the United States Census Bureau's survey results [16] indicate that 38% of the U.S. population commutes to work through carpooling, making
it a significant mode of transportation in the country. However, in contrast, several
regions lack comprehensive data on carpooling, suggesting significantly lower carpooling
participation rates. Factors such as the absence of dedicated occupancy lanes for
vehicles with multiple riders contribute to these lower carpooling rates, making the
establishment of an open-community carpooling system in these regions challenging.
Instead, a closed-community ridesharing system designed to serve specific, smaller
populations like colleges or residential areas holds the potential for creating a
carpooling community.
4. Functionality of Carpooling Systems
Carpooling services involve registering users as passengers or drivers on the online
platform's site. Users can then post a ride or select from the available ride listings
posted by other members. Users exchange the requisite fare to confirm the booking
upon agreement on the travel details, including location and departure time. Typically,
rides are available for a specific time and place, but passengers can often adjust
their routes and departure times for an additional fee. Users may also provide reviews
and ratings for drivers, facilitating transparency and trust-building within the community.
Carpooling services are not operated for commercial purposes, and many countries have
laws permitting drivers to charge only their actual expenses. Drivers typically calculate
costs based on factors such as the route and the type of vehicle used, resulting in
significantly more cost-effective travel than private cars or public transportation.
Carpooling services are categorized differently from car-sharing services and are
contingent on trip length and frequency [16].
5. Carpooling Systems: A Digital Realm
Carpooling systems manifest as digital platforms accessible through websites, applications,
or a combination of both, hosted on the internet and accessible via various devices.
Several carpooling systems operate within social media platforms such as Facebook
and Instagram or through their independent interfaces. While some of these systems
extend their services to various regions, they do not currently boast large user bases,
and there is a noticeable absence of carpooling systems explicitly designed for certain
areas. These carpooling systems informally fall into two broad categories: open-community
carpooling systems allowing users to register and employ platforms like Google Maps
and closed-type car-sharing systems tailored to specific smaller communities, where
membership criteria must be met for participation [17].
6. Various Forms of Carpooling
Carpooling manifests in several distinct forms, contingent on how joint trips are
planned. In regular carpooling, departure times and locations are fixed and unalterable.
Classic carpooling involves planning trips one or several days in advance, often spanning
longer distances, typically from a kilometer to several kilometers. Dynamic carpooling
functions within urban regions and for short distances, with various transportation
options, such as private vehicles, public transportation, cabs, walking, motorbikes,
and more [18].
Carpooling systems are frequently used for travel among strangers and, except for
the first category, can introduce complexities and necessitate robust solutions. Real-time
or dynamic ridesharing solutions are often associated with these categories, and they
may involve mobile applications that facilitate impromptu meetings and immediate journey
commencements, with passengers contributing a portion of the trip cost.
7. Availability of Carpooling Systems
A variety of web-based carpooling systems operate worldwide [19]. Several noteworthy carpooling and ride-sharing platforms include Pickup Pal, headquartered
in the United States, which utilizes Google Maps for location display and offers an
engaging user interface. Avego is a mobile-based system with GPS capability for tracking
transactions, users, drivers, and trips. RideShare, operating in Washington, is an
application that focuses on scheduled carpooling. Piggyback is a Facebook carpooling
app featuring an efficient rating system that allows users to rate one another on
specific aspects like driving skills. Lastly, GoLoco is a system that integrates with
Facebook and is particularly useful for pre-planned trips, seamlessly combining social
elements with trip planning.
8. Challenges and Concerns In Carpooling Systems
The successful implementation of carpooling systems hinges on addressing various challenges
and concerns as shown in Fig. 5.
Fig. 5. Challenges in smart carpooling.
Security: Carpooling systems, in essence, entail traveling with strangers, which can
raise security concerns. Implementing security measures such as auto event recorders,
camera feeds, RFID, fingerprints, voice recognition, and real-time map tracking has
been suggested to mitigate these issues [20]. However, these solutions may be costly and technically challenging.
Trust: Trust is a fundamental concern in carpooling systems due to the involvement
of unknown individuals. Implementing a robust scoring system in which users rate each
other on various aspects is recommended to build trust among users.
Exchange of money: The exchange of money can be challenging, as some individuals may
find it problematic or untrustworthy to conduct cash transactions with strangers.
Integrating payment systems into the carpooling platform is logical; however, online
payment systems can raise user security concerns.
Legality: Carpooling systems can face legal challenges, as exemplified by PickupPal's
conflict with the Public Vehicles Act in the United States. Addressing legal issues
and establishing rules is essential to avoid potential legal disputes [21].
9. Previous Studies
Over the past few decades, the carpooling problem has garnered significant attention
from researchers, fueled by advancements in information technology. various studies
now exist addressing various aspects of carpooling. Notably, Agatz et al. [19] offer a comprehensive overview of relevant literature on carpooling. The literature
on carpooling problems explores diverse objective functions [21]. Some studies focus on reducing fixed vehicle costs and network travel expenses,
while others consider carpooling services in the context of penalty costs related
to delays [22]. Addressing the issue of fines for unsecured claims is another area of research.
To tackle the NP-hard complexity problem, the majority of previous studies employ
heuristic solution approaches [23]. Furthermore, some of the studies have focused on the exact algorithm of carpooling
[24]. Notable prior studies in this domain include optimization-based methods to reduce
the costs of pairing passengers and drivers, a carpooling service designed for university
staff, teachers, and students, and the application of network flow and time-space
methodology to various vehicle and individual forms [41-43]. Some studies also delve into carpooling based on GPS trajectories, while others
tackle issues like common route planning for carpoolers [25]. Additionally, research efforts explore multi-hop ride-matching, reducing detours
in carpooling routes, and assessing acceptable benefits and pricing strategies for
multi-carpooling systems [26]. Furthermore, researchers have proposed innovative algorithms and mathematical formulas
to address specific aspects of carpooling, such as reducing CO2 emissions [27]. The introduction of mobile applications to support carpooling services also signifies
a significant development in the field. Previous research highlights the multifaceted
nature of the carpooling problem, with researchers applying diverse approaches and
methodologies to tackle its complex challenges.
9.1. Comparison of Previous Studies
We present a comparative analysis of previous studies and the algorithms they employed
in the realm of carpooling [28]. Each study had specific objectives summarized in Table 1 below with their relevant details. The analysis of previous studies in carpooling
reveals various assumptions and methodologies. Most studies consider full-time drivers
picking up all passengers without being influenced by transportation demand, and cars
typically do not belong to the carpoolers themselves. In some studies, a subset of
riders can supply cars for carpooling activities, and the model determines which riders
will serve as drivers and pick up others. This approach increases the problem's complexity
but enhances practicality. Moreover, heuristic and meta-heuristic methods have gained
significant traction in addressing carpooling problems. These techniques are particularly
favored for their flexibility and efficiency in solving the intricate challenges presented
by carpooling scenarios. In the context of the literature mentioned above, it is evident
that carpooling research encompasses three distinct fields i) Ride Matching Challenges
and Solutions in Carpooling ii) Artificial Intelligence in Carpooling, and iii) Security
Enhancement. All the research trends are summarized in Table 2 below to highlight the aim, algorithms, and advantages of the literature work done
in these areas.
Table 1. Comparison of previous studies and algorithms in carpooling.
|
Authors/Objectives
|
Penalty for delay
|
Fixed price of vehicles
|
Unserved demands penalty cost
|
Traveling on network
|
Are the cars well-known in advance?
|
Algorithm/Solution approach
|
|
Pelzer et al.[29]
|
No
|
No
|
No
|
Yes
|
Yes
|
Heuristic
|
|
Ghoseiri et al. [33]
|
No
|
No
|
No
|
Yes
|
Yes
|
Three-Spherical method
|
|
Guo et al. [34]
|
Yes
|
No
|
Yes
|
Yes
|
No
|
Ant colony
|
|
He et al. [31]
|
No
|
No
|
No
|
Yes
|
Yes
|
Heuristic algorithms
|
|
Xia et al. [25]
|
No
|
No
|
No
|
Yes
|
Yes
|
Metaheuristic
|
|
Zhang et al. [22]
|
No
|
Yes
|
Yes
|
Yes
|
Yes
|
Mathematical model
|
|
Huang et al. [23]
|
No
|
No
|
No
|
Yes
|
Yes
|
Genetic algorithm
|
|
Huang et al. [20]
|
No
|
Yes
|
No
|
Yes
|
No
|
Tabu search algorithm
|
|
Najmi et al. [21]
|
No
|
No
|
No
|
yes
|
Yes
|
Clustering heuristic
|
|
Bruglieri et al.[28]
|
No
|
No
|
No
|
Yes
|
Yes
|
Guided Monte-Carlo method
|
|
Masoud et al. [26]
|
No
|
Yes
|
No
|
Yes
|
Yes
|
Decomposition algorithm
|
Table 2. Research trends in carpooling.
|
Research Trends
|
Ref. No
|
Aim
|
Algorithms
|
Advantages
|
|
Ride Matching Challenges and Solutions in Carpooling
|
Agatz et al. [37]
|
Dynamic ride matching
|
Optimization techniques
|
One delivery and pickup capacity
|
|
Herbawi et al. [38]
|
Dynamic ride matching
|
Genetic algorithm
|
Real-world implementation
|
|
C. Huang [35]
|
Ride matching
|
Mathematical model
|
The transition between multiple rides
|
|
Artificial Intelligence in Carpooling
|
Jindal et al. [39]
|
Optimal
Carpooling strategies
|
Reinforcement
Learning
|
Maximizing transportation efficiency
|
|
Santos et al. [40]
|
User preferences
|
Machine learning
|
Selecting ideal travel partners
|
|
Tianyang et al. [41]
|
Tracking moving objects to the destination
|
Dynamic adaptive k-nearest neighbors (DAKNN)
|
Enhances query accuracy
|
|
Papoutsis et al. [42]
|
Dynamic car-sharing services
|
Multi-layer Bayesian hierarchical model
|
Anticipation of waiting times
|
|
Samy et al. [43]
|
Travel profiles and Location semantics
|
Machine learning
|
Reduction in total number of trips
|
|
Security Enhancement.
|
D. Wang et al. [44]
|
Security
|
Consortium Blockchain Security
|
Safeguard data interactions
|
|
H. Yu et al. [45]
|
Privacy-Preserving Ride Matching
|
Optimization technique
|
Minimize extra trip time and enhance location privacy
|
|
J. Friginal et al. [46]
|
Optimized taxi scheduling
|
Principles of privacy-by-design
|
Minimum road Trip estimation
|
|
P. Goel et al. [47]
|
Privacy by Design
|
Recursive ellipse
|
Confidentiality
|
|
H. Zhang et al. [48]
|
Blockchain billing security
|
Publicly-verifiable two-party blockchain.
|
Eliminates the need for an online third-party
|
|
A. Kleiner et al. [49]
|
Encrypted data coordination
|
Sealed-bid second price scheme.
|
No compromising personal information
|
10. ICT in Carpooling
Effective carpooling relies on the ability of commuters to communicate and coordinate
with one another, a pivotal concept explored in the realm of Information and Communication
Technology (ICT), particularly web-based ride-matching platforms. Research conducted
in the mid-1990s reflects commuters' initial enthusiasm for carpooling while grappling
with the challenges of finding compatible matches [50]. However, the practical utility of web-based carpool applications during that era
appeared limited, as successful match outcomes were scarce. Despite the simultaneous
rise of widespread Internet usage, the chances of forming a carpool were merely one
in five at best. This aspect is particularly intriguing because the timeframe of this
study in the late 1990s coincided with the widespread adoption of the Internet [51]. Subsequent research on ride-matching found that an increase in user registrations
directly led to higher carpool formation rates. This result aligns with intuition:
with a growing user base, the pool of available commuting routes and commuter profiles
naturally expands, increasing the likelihood of finding suitable carpool matches.
The research suggests that while a limited carpool program may initially have a limited
impact, its effectiveness grows over time as more commuters participate, highlighting
the potential for substantial positive change. Interestingly, the rise of ridesharing
appears closely linked to shifts in social norms, with technology playing a promising
role in carpool development. Notably, a study conducted in Europe indicated the success
of ICT-based solutions, even though it lacked empirical evidence to substantiate these
claims [52].
11. Benefits of Carpooling Systems
Carpooling systems present many advantages that are often overlooked, yet they are
pivotal in addressing contemporary transportation challenges as shown in Fig. 6. The dynamics of carpooling have evolved significantly in recent years, thanks to
advancements in information technology, cellular services, social networking, and
cloud technologies, ushering in a new era of shared and demand-based mobility. These
transformative developments have rekindled interest in IT-driven carpooling, giving
rise to a host of social, employer, and individual benefits.
Fig. 6. Benefits of carpooling.
Mitigating traffic congestion: Carpooling systems have emerged as a powerful antidote
to the perennial issue of traffic congestion. By reducing the number of vehicles on
the road, carpooling eases traffic volumes and minimizes gridlock, contributing to
smoother urban transit. Energy Efficiency: Carpooling is a beacon of energy conservation.
In a world where vehicles consume an average of 550 gallons of fuel annually, carpooling
offers a promising solution for reducing fuel consumption in densely populated urban
areas. This curbs energy usage and reduces air pollution and cost savings in gasoline
and fees.
Curbing greenhouse gas emissions: Carpooling champions the battle against greenhouse
gas emissions (GHG). Studies have consistently shown that carpooling significantly
reduces GHG emissions, benefiting the global environment and contributing to a reduced
carbon footprint.
Fuel savings and environmental impact: On the global stage, the impact of carpooling
systems is profound. It results in fuel savings and has a cascading effect on various
environmental aspects. For instance, research reveals that obese and overweight individuals
in the United States consume an additional billion liters of fuel annually, amounting
to 0.8% of the total fuel consumed by noncommercial passenger vehicles. Implementing
carpooling with just one extra passenger for every ten vehicles could save 7.5 to
7.7 billion gallons of fuel annually in the United States. Additionally, studies have
shown that employer trip reduction programs can reduce GHG emissions by approximately
4 to 5%.
Lower parking demand: Carpooling helps lower workplace parking demand, leading to
significant cost savings in capital expenditures and operational maintenance.
Financial tax incentives: Employers who promote carpooling can benefit from financial
tax advantages, providing an added incentive for organizations to encourage carpooling
among their staff.
Stress reduction and cost-sharing: Commute stress reduction, cost-sharing, and efficient
utilization of high-occupancy vehicle lanes contribute to improved work environment
and employee morale.
Ensuring social justice: Equal access to transportation carpooling is pivotal in achieving
social justice by bridging transportation disparities between individuals of different
economic backgrounds. It empowers individuals, including those with limited financial
means, to access transportation without owning a personal vehicle, fostering greater
equality in mobility and transportation accessibility [53].
12. Conclusion
Carpooling has a huge potential that can change over time, which ultimately reshapes
the transportation mode and is beneficial in terms of fuel prices and many other factors.
Carpooling becomes more evident while living in metropolitan cities where congested
transportation and lack of parking facilities always remain an issue for personal
car owners. Carpooling and sharing cars can help reduce traffic and pollution, making
city transportation systems more sustainable. Carpooling also requires significant
attention towards suitable legislation at the macro and micro level to facilitate
the common user to enhance this trend keeping safety, trust, and social justice as
main stakeholders as this possesses ample benefits in the economy and various aspects
of society.
13. Future Research Directions
One of the hot future research directions is the connected car, where a passenger
can reach his destination by using multiple cars that are traveling different routes,
however, this can accommodate the passengers after carefully computing the availability,
the drop-off location, the capacity of the vehicle, and especially the time constraints,
to less wastage of the passenger precious time. To achieve this goal, smart carpooling
requires embedding friendly interacting intelligent apps, AI-based robust route prediction
algorithms, and keeping the preferences and the routing of the passenger in the eye
while designing the policies of the carpooling.
ACKNOWLEDGMENTS
This work was supported by the National Research Foundation (NRF), Korea, under project
BK21 FOUR.
The first author would like to thank the Joint Graduate School of Energy and Environment
(JGSEE) and Sirindhorn International Institute of Technology (SIIT) for supporting
the Ph.D. scholarship.
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Author
Adeel Munawar received his M.S. degree in computer science from Government College
University (GCU), Lahore, Pakistan, in 2018. His research interests encompass multi-agent
systems, Artificial Intelligence, real-time systems, machine learning, and Natural
Language Processing. Presently, he serves as a senior lecturer in Computer Science
at Lahore Garrison University, actively engaged in research activities to promote
research advancements. Additionally, he is currently pursuing a Ph.D. degree from
Sirindhorn International Institute of Technology, Thammasat University, Thailand.
Mongkut Piantanakulchai received his M.Eng. degree in transportation from the Asian
Institute of Technology (AIT), Thailand. He received a Ph.D. degree in transportation
from Tohoku University, Japan. Currently, he is working as an associate professor
at Sirindhorn International Institute of Technology (SIIT). His research interest
includes Intelligent transportation systems (ITS), multi-criteria decision-making
in transportation planning, activity-based travel demand modeling, and fuzzy decision-making
methods.
Muhammad Nadeem Ali is currently pursuing a Ph.D. degree at Hongik University, South
Korea. He completed his B.S. and M.S. degrees in electronics engineering from the
International Islamic University Islamabad in 2012 and 2016, respectively. His BS
in Electronics Engineering was fully funded by the Ministry of Information and Technology,
Government of Pakistan, for four years. The scholarship name was ICT & RD Fund. Currently,
he is a member of the Broadband Convergence Network Laboratory at Hongik University.
His major interests are 5G wireless communication systems, intelligent transport systems
edge computing, and Named Data Networks.
Byung-Seo Kim (M02-SM17) received his B.S. degree in electrical engineering from In-Ha
University, Korea, in 1998 and his M.S. and Ph.D. degrees in ECE from the University
of Florida in 2001 and 2004, respectively. Between 1997 and 1999, he worked for Motorola
Korea Ltd., Korea, in ATR&D, and from January 2005 to August 2007, worked for Motorola
Inc., Schaumburg Illinois, in Networks and Enterprises. From 2007, he has been a professor
in Department of Software and Communications Engineering, Hongik University, South
Korea. He is serving as associate editor of IEEE Access, Telecommunication systems,
and Journal of the Institute of Electrics and Information Engineers. He is an IEEE
Senior Member.