Rendezvous Sequence Generation Algorithm for Cognitive Radio Networks in Post-Disaster Scenario

International Journal of Computer Networks & Communications (IJCNC) Vol.16, No.3, May 2024
DOI: 10.5121/ijcnc.2024.16302 17
RENDEZVOUS SEQUENCE GENERATION
ALGORITHM FOR COGNITIVE RADIO NETWORKS IN
POST-DISASTER SCENARIO
Junhyung Kim
Department of Software and Convergence, Yeungnam University College, Daegu, Korea
ABSTRACT
Recent natural disasters have inflicted tremendous damage on humanity, with their scale progressively
increasing and leading to numerous casualties. Events such as earthquakes can trigger secondary disasters,
such as tsunamis, further complicating the situation by destroying communication infrastructures. This
destruction impedes the dissemination of information about secondary disasters and complicates post-
disaster rescue efforts. Consequently, there is an urgent demand for technologies capable of substituting for
these destroyed communication infrastructures. This paper proposes a technique for generating rendezvous
sequences to swiftly reconnect communication infrastructures in post-disaster scenarios. We compare the
time required for rendezvous using the proposed technique against existing methods and analyze the average
time taken to establish links with the rendezvous technique, discussing its significance. This research presents
a novel approach enabling rapid recovery of destroyed communication infrastructures in disaster
environments through Cognitive Radio Network (CRN) technology, showcasing the potential to significantly
improve disaster response and recovery efforts. The proposed method reduces the time for the rendezvous
compared to existing methods, suggesting that it can enhance the efficiency of rescue operations in post-
disaster scenarios and contribute to life-saving efforts.
KEYWORDS
Cognitive Radio Networks, Rendezvous Sequence, Post-disaster Scenario
1. INTRODUCTION
Globally, natural disasters such as heatwaves, droughts, floods, and wildfires pose significant
challenges, causing severe harm to humanity through considerable casualties and economic
losses. For instance, in 2022, floods in Pakistan resulted in at least 17,000 fatalities, while
Hurricane Ian in the United States led to estimated damages of around 100 billion dollars. These
disasters not only have immediate impacts but also lead to secondary crises, like tsunamis
following earthquakes, which further devastate local communication infrastructures. Such
destruction severely hinders rescue efforts and the delivery of vital information regarding
impending secondary hazards.
In the aftermath of these events, one of the paramount challenges is the swift re-establishment of
communication networks to support effective rescue and recovery operations. This necessity
brings Cognitive Radio Networks (CRNs) to the forefront, showcasing their critical role in such
scenarios. CRNs, with their dynamic spectrum access capabilities, present a robust solution for
reconfiguring communication links where traditional networks have failed. This paper addresses
these post-disaster communication challenges by proposing an algorithm that utilizes rendezvous
technology within CRNs. Our approach aims to rapidly reconstruct communication
infrastructures in disaster-affected areas, thereby significantly improving the coordination and

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efficiency of disaster response efforts. Through this work, we highlight the invaluable role of
CRNs in overcoming the communication barriers faced in post-disaster scenarios, offering a path
towards more resilient disaster recovery processes.
2. RELATED WORKS
2.1. Cognitive Radio Networks
Cognitive Radio Networks (CRN) have emerged as an innovative solution to the spectrum
scarcity problem in wireless communication systems [1], [2], [3]. Traditionally, the wireless
frequency spectrum has been a finite and heavily regulated resource, leading to inefficient
spectrum utilization. CRNs introduce intelligence and adaptability into wireless devices, enabling
dynamic access to underutilized spectrum bands and enhancing overall spectrum efficiency [4]
[5]. At the heart of CRN is the concept of Dynamic Spectrum Access (DSA), which allows
cognitive wireless devices to opportunistically access available spectrum bands [6]. This dynamic
approach ensures efficient utilization of spectrum resources, especially in areas where the
Primary Users (PUs or licensed users) do not fully exploit the spectrum.
Spectrum sensing is a fundamental component of CRN, where cognitive radios continuously
monitor the frequency spectrum [7]. Through sensing, these wireless devices can detect channels
that are empty or underutilized, adjusting in real-time to the wireless environment. The cognitive
cycle comprises three main phases: sensing, decision-making, and reconfiguration. Cognitive
radios scan the spectrum conditions, determine based on observed data, and dynamically
configure transmission parameters to optimize performance. This adaptive cycle guarantees
efficient spectrum utilization in dynamic and unpredictable environments.
Figure 1. Cognitive Radio Networks in post-disaster Scenarios
Secondary Users (SU), or unlicensed devices, serve a pivotal role in CRNs. These devices utilize
the spectrum opportunistically without inducing harmful interference to PU. The adaptability of
SU is a key feature in maximizing spectrum utilization. There are several challenges to overcome
in CRN research [8]. The first is the challenge of spectral sensing. Accurate spectrum sensing
encounters challenges such as noise, fading, and hidden terminal issues. Ongoing research

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investigates advanced sensing techniques, including cooperative sensing and machine learning
algorithms, to improve the reliability of spectrum sensing in CRNs [9]. The second is related to
Interference Mitigation. To mitigate interference to PU, CRNs utilize sophisticated interference
management techniques. Dynamic power control, spectrum sensing databases, and
communication protocols that prioritize PU are implemented to maintain coexistence and
minimize disruption [10]. The third challenge is Rendezvous Mechanisms. Efficient rendezvous
mechanisms are critical for cognitive radios to initiate communication. Various strategies,
including time-based, frequency-based, and hybrid approaches, are examined to facilitate reliable
communication initiation among SUs [11].
There are numerous application domains for Cognitive Radio Networks (CRN), each showcasing
its versatility and potential. The primary area is wireless communication. CRNs are instrumental
across a spectrum of wireless communication frameworks, encompassing cellular networks, ad
hoc networks, and the Internet of Things (IoT). Their adaptability makes them exceptionally
suitable for diverse communication environments [12]. Another critical application is in Public
Safety and Emergency Communication. Here, CRNs bolster public safety communications by
dynamically allocating spectrum resources to emergency services in times of critical need. This
flexibility is pivotal in ensuring uninterrupted communication, especially when conventional
networks face congestion or are otherwise compromised [13].
CRNs epitomize a transformative shift in wireless communication, embodying a dynamic and
intelligent methodology for spectrum utilization. Fueled by continuous advancements in spectrum
sensing, interference management, and rendezvous mechanisms, CRNs stand at the forefront,
poised to significantly influence the burgeoning demand for wireless services and sculpt the
future landscape of communication systems.
2.2. Rendezvous
CRNs have been introduced as an innovative approach to mitigate the increasing demand for
wireless communication and the scarcity of available spectrum resources [14]. In CRNs,
Secondary Users (SUs) opportunistically access underutilized spectrum bands, dynamically
adapting to the changing wireless environment [15]. A core challenge in CRN operations is the
establishment of rendezvous points where cognitive radios can efficiently converge and exchange
control information to commence communication. The rendezvous concept is pivotal within
CRNs, facilitating seamless identification and linkage among cognitive radio devices. In the
absence of effective rendezvous mechanisms, cognitive radios might face difficulties in
synchronizing and establishing communication links, which could result in suboptimal spectrum
utilization.
A broad spectrum of rendezvous mechanisms has been developed and explored [16], each
presenting distinct benefits and drawbacks. A segment of these mechanisms depends on temporal
synchronization, identified as time-based rendezvous mechanisms. In such methodologies,
cognitive radio apparatuses are orchestrated to embark on communication at predetermined time
intervals. Although time-based strategies facilitate the streamlining of coordination, they may
confront challenges in achieving precise synchronization, particularly in fluctuating and
indeterminate wireless contexts.
Conversely, a different rendezvous strategy concentrates on frequency synchronization, in which
a mutual channel is designated for rendezvous, permitting cognitive radios to engage in
communication upon harmonizing to the same frequency. Frequency-based schemes provide
merits in straightforwardness and diminished synchronization necessities, yet they might grapple
with interference complications and the hidden terminal issue.

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Hybrid methodologies integrate aspects of both time and frequency synchronization, end
eavoring to capitalize on the strengths inherent in each strategy to furnish a more adaptable and
resilient solution. Hybrid rendezvous mechanisms strive to alleviate the shortcomings linked with
solely time-based or frequency-based approaches.
Notwithstanding the variety of rendezvous mechanisms, numerous challenges persist within
CRNs, encompassing synchronization difficulties, the hidden terminal conundrum, and the
imperative for proficient spectrum sensing. The research community is vigorously seeking
innovative resolutions to surmount these obstacles and enhance the dependability and scalability
of cognitive radio networks. Recent progress in this domain involves the investigation of machine
learning-driven rendezvous strategies, cooperative sensing approaches, and decentralized
algorithms. These advancements seek to boost rendezvous efficiency in dynamic and intricate
wireless landscapes, thus augmenting the overall efficacy of CRNs [17].
Instituting efficacious rendezvous mechanisms constitutes a pivotal element for the triumph of
cognitive radio networks. Time-based, frequency-based, and hybrid modalities proffer distinct
advantages, with current research endeavors aimed at rectifying prevalent issues and propelling
forward innovative technologies. As CRNs undergo further advancement, the formulation of
sturdy rendezvous mechanisms will assume a critical function in guaranteeing the efficient and
dependable employment of spectrum resources, thereby fulfilling the escalating requisites of
wireless communication.
2.3. Rendezvous Issue
Rendezvous mechanisms face numerous challenges that require comprehensive solutions,
including dynamic frequency conditions and energy efficiency. The formulation of rendezvous
algorithms must address these concerns. The primary challenge is the dynamic frequency
landscape, characterized by frequent shifts in frequency bands. Such volatility complicates the
coordination among wireless devices for effective rendezvous, emphasizing the need for
algorithms capable of adapting to these changes swiftly.
Another significant concern is the delay in rendezvous. Certain algorithms, especially those with
difficulties in maintaining precise time synchronization, may experience delays, disrupting the
coordinated timing necessary for effective device rendezvous and thereby impairing efficient
frequency use.
Energy consumption presents a third hurdle. The process of rendezvous, encompassing message
transmission and frequency scanning, incurs energy usage. Excessive energy consumption can
diminish battery lifespan and curtail the operational longevity of wireless devices. Thus,
algorithms must incorporate strategies for efficient energy utilization, though this is an area
where some algorithms may fall short.
Lastly, the challenge of adaptability in dynamic environments is notable. Several algorithms do
not adequately adjust to the rapid shifts in the frequency landscape, which can compromise the
efficiency of the rendezvous process and result in suboptimal frequency resource utilization.
To surmount these obstacles, rendezvous strategies need to be adaptable to fluctuating frequency
environments and incorporate efficient energy management. The development of practical and
efficacious rendezvous algorithms that account for these critical factors is essential for the
seamless operation of cognitive radio networks.

21
2.4. Problems
Recent patterns in natural disasters reveal a sequence where a primary event, such as an
earthquake or flood, precipitates secondary calamities, including aftershocks or tsunamis.
Following a primary disaster, the urgent dissemination of information regarding imminent
secondary disasters to the inhabitants of the affected zones becomes paramount. This necessitates
the rapid reconstitution of communication links to replace the network infrastructure obliterated
by the primary disaster. The deployment of CRN Nodes and Mobile CRN Nodes is critical for the
swift re-establishment of communication facilities. Within the disaster-stricken areas, CRN
Nodes, Mobile CRN Nodes, and traditional nodes are tactically positioned. CRN Nodes and
Mobile CRN Nodes within reachable distances strive to forge communication links through a
designated Rendezvous sequence. This document introduces an algorithm for crafting
Rendezvous sequences for use by CRN nodes, with the objective of minimizing the time and
energy expenditure involved in Rendezvous processes.
3. PROPOSED SCHEME
For two nodes in a CRN to rendezvous without prior knowledge of each other's information, they
must visit channels according to a specified channel hopping algorithm. Various techniques have
been introduced to generate channel hopping sequences for successful rendezvous within a given
time frame. This paper aims to introduce methods for generating sequences and rendezvous
techniques to establish communication paths between two nodes without mutual information,
particularly in disaster scenarios.
In disaster situations, communication infrastructure is often destroyed, making it impossible to
exchange disaster-related information. This leads to difficulties in providing information about
secondary disasters that may occur after the initial catastrophe. Given the potential for secondary
disasters at any time following the primary event, rapid information dissemination becomes
crucial. Moreover, after the conclusion of the disaster situation, there is a need for swift
reconfiguration of communication infrastructure for relief activities. Therefore, the proposed
algorithm is essential for the rapid restructuring of communication infrastructure.
3.1. Definition
The proposed system is operational within the CRN framework and is engineered to become
active during disaster scenarios. The channels of this system represent the available frequency
bands or spectra suitable for communication within the cognitive wireless network. The symbol
N represents the aggregate count of accessible channels, with the sequence of these channels
being indicated as {CH1, CH2, CH3, ..., CHN}.
Within this architecture, a "slot" is characterized as a brief duration in the frequency spectrum
that is efficiently employed in the Rendezvous process to enhance time division optimization.
The term "time lag," symbolized by k, delineates the temporal discrepancy in intervals that nodes
allocate for the rendezvous. The time lag k is articulated as a non-negative integer, quantified by
the tally of time slots. A "node" is delineated as a communicative apparatus endowed with the
capability to perform wireless data transmission and reception via an interface predicated on CR
technology.
As previously elucidated, PU denotes the licensed proprietors of the frequency band channel,
whereas SU signifies the node that provisionally exploit the frequency band in the absence of
PU's active engagement. The Time to Rendezvous (TTR) metric gauges the span of time slots

22
dedicated to data transmission and reception during the rendezvous phase. Maximum Time to
Rendezvous (MTTR) corresponds to the total number of time slots required for communication
between two nodes attempting to establish a link. Average Time to Rendezvous (ATTR) is
defined as the average number of time slots needed for a successful rendezvous.
3.2. Sequence Generation Algorithm
In this section, we aim to explain the proposed algorithm, specifically addressing the aspects
related to generating sequences for channel configuration. The proposed algorithm constitutes a
method for swiftly reconstructing the network infrastructure in disaster-stricken areas, focusing
on sequence generation for channel setup. Two nodes desiring communication attempt
Rendezvous using sequences generated by the proposed algorithm to establish a communication
link. Nodes follow the suggested sequence, visiting channels to attempt Rendezvous. They wait
for a predefined period on each channel, anticipating signals from the desired peer node for
communication. If no signal is detected on a particular channel within the specified waiting time,
nodes transition to the next channel according to the sequence to initiate Rendezvous again. This
iterative process continues until both nodes successfully receive and exchange signals on the
same channel, concluding with the establishment of a communication link for data transmission
through the identified channel that the two nodes discovered for communication.
The initial phase of sequence generation involves the collection of channel information. Nodes at
disaster sites commence by sensing the spectrum of the current frequency band to gather
information on the channels. This sensing process enables the acquisition of data regarding
channels that are currently in use and those that are not. Nodes determine the availability of each
channel by detecting the presence or absence of signals from other nodes on those channels. The
information collected is essential for the creation of the rendezvous sequence and is, therefore,
organized and maintained in a separate list. Utilizing this list, the Rendezvous Sequence is
generated.
In this process, each node operates according to its assigned role. The Mobile Node assumes the
responsibility of relaying data in the newly formed network. Nodes assigned to the relaying task
attempt rendezvous through the proposed algorithm to connect with more nodes.
The algorithm generation method for the Mobile Node is as follows. Each node secures a list of
available channels and generates a Hopping sequence. The Hopping sequence is generated as
follows based on the proposed algorithm. Initially, the algorithm creates channel sets by
incrementally adding channels one by one up to the total number of available channels. The
purpose of creating a channel set is to facilitate uniform visits to channels during rendezvous
times.
The detailed method for creating channel sets is as follows: Initially, the first channel from the
available channel list is selected to establish the first channel set. Subsequently, the first and
second channels are combined to form the second channel set. The process continues by
incorporating the first, second, and third channels to create the third channel set. This procedure
is repeated until the final channel in the channel list is included. Thus, if there are n available
channels, a total of n channel sets will be generated.
For instance, if N = n, the channel sets are formed as follows: {CH1}, {CH1, CH2}, {CH1, CH2,
CH3}, …, {CH1, CH2, …, CHn}. The generated channel sets are then outputted in reverse order as
follows:
{CH1, CH2, …, CHn}, {CH1, CH2, …, CHn-1}, …, {CH1, CH2}, {CH1}

23
Next, the front part of the outputted channel sets is connected by sequentially appending the
channels from the channel list. If N=n, the channel sets are modified as follows:
{CH1, CH1, CH2, …, CHn}, {CH2, CH1, CH2, …, CHn-1}, {CH3, CH1, CH2, …, CHn-2}, …, {CHn,
CH1}
Now, the entire set of channel sets is concatenated into one. When N=n, connecting the complete
channel set results in the following sequence:
{CH1, CH1, CH2, …, CHn, CH2, CH1, CH2, …, CHn-1, CH3, CH1, CH2, …, CHn-2, …, CHn, CH1}
The following step involves the creation of a Guard set designed to prevent Rendezvous failures.
The methodology for constructing this Guard set is derived from the approach proposed in HS-
GRSP [18]. Instances of rendezvous failures have been observed in previously suggested
rendezvous sequence generation algorithms. This algorithm is introduced as a preventive measure
against such occurrences. To create the Guard sequence, the number of collected channels is a
prerequisite. Simply form a set by repeating the first channel of the channel list as many times as
the number of channels. If N=n, and the first channel in the channel list is CH1, the Guard
sequence would be as follows:
{CH1, CH1, CH1, …, CHn}
The final step is to merge the two previously generated channel sets. In the case where N=n, the
resulting merged channel set is as follows:
{CH1, CH1, CH2, …, CHn, CH2, CH1, CH2, …, CHn-1, CH3, CH1, CH2, …, CHn-2, …, CHn, CH1,
CH1, CH1, CH1, …, CHn}
The sequence generation process up to this point is exemplified below. Assuming an available
channel count of N=3, the sequence creation process is outlined in Table 1.
Table 1. Sequence Generation Step.
Title Generated sequence
Creating channel sets {CH1}, {CH1, CH2}, {CH1, CH2, CH3}
Generating ordered channel
sets
{CH1, CH1, CH2, CH3}, {CH2, CH1, CH2}, {CH3, CH1}
Connecting channel sets {CH1, CH1, CH2, CH3, CH2, CH1, CH2, CH3, CH1}
Creating a guard set {CH1, CH1, CH1}
Merge {CH1, CH1, CH2, CH3, CH2, CH1, CH2, CH3, CH1, CH1, CH1, CH1}
4. PERFORMANCE EVALUATION
This section aims to evaluate the proposed algorithm focusing on TTR as the key performance
metric. The performance is compared with existing methods available in the current literature,
specifically SBR [19], CRSEQ [20], and HS-GRSP. The evaluation is carried out through
simulations in a simulation environment assuming about 30 CRN Rendezvous channels. PU can
occupy each channel at given times. It is assumed that all SU are within communication range.
Each node visits the channels according to the proposed rendezvous sequence. At this time, if two
nodes successfully send packets on the same channel, a link for data transmission is established.
It is assumed that the wireless communication devices of the two nodes wishing to communicate
are powered on, and their batteries are sufficiently charged. Additionally, the ambient noise

24
levels are considered negligible and do not interfere with the communication process. These
assumptions were made in conducting the experiment. The simulation parameters are detailed in
Table 2 for reference.
Table 2. Simulation parameters
Parameters Values
Number of Channels 30
SU Range All nodes are within communication range
Channel Hopping Nodes hop through channels using proposed sequence
Data Exchange Exchange data packets upon obtaining a common channel during hopping
The simulation was iteratively conducted for a number of repetitions equal to the length of the
rendezvous sequence. During the experiments, the value of k was incrementally increased in
accordance with the length of the rendezvous, executing the tests accordingly. In instances where
N equals n, the experiment was carried out by incrementing k by one for each value up to n.
However, for some algorithms, an attempt to increase k while performing the rendezvous led to
issues with achieving a rendezvous. Consequently, the experiment was adjusted to repeat the
rendezvous sequence twice, ensuring the rendezvous was successfully established.
We will assess the performance of the proposed algorithm based on the Average Time to
Rendezvous (ATTR) and Maximum Time to Rendezvous (MTTR) metrics. The TTR is already
used as a performance metric in thousands of rendezvous-related research papers [21], [22].
Figure 2. An example of Rendezvous (N=3, k=2)
Figure 2 demonstrates how the proposed algorithm successfully achieves a rendezvous. Nodes A
and B generate rendezvous sequences according to the proposed algorithm, assuming N=3. Node
A begins channel searching for the rendezvous according to the generated sequence. Assuming
k=2 for Node B, it starts its channel search for the rendezvous two slots later. As illustrated,
following the proposed rendezvous sequence, both nodes visit the channel and successfully
rendezvous in the fifth slot.
4.1. TTR Performance
We compared the performance of the proposed algorithm in terms of TTR. TTR is the time it
takes for two CR nodes to rendezvous. TTR is measured in the number of time slots elapsed from
the start time to the rendezvous. The MTTR is the maximum TTR required to achieve a
rendezvous. In the graph, N represents the number of available channels.
Figure 3 presents the experimental results for the MTTR, which signifies the longest duration
required to achieve a rendezvous. Both the HS-GRSP and the proposed algorithm exhibit the
lowest MTTR values, indicating that the proposed algorithm can achieve rendezvous more
rapidly compared to other algorithms. In Figure 3, the MTTR for CRSEQ is observed to be the
highest, suggesting it may take the longest time to achieve a rendezvous.

25
Figure 3. MTTR
Figure 4 illustrates the ATTR. It can be observed from Figure 4 that CRSEQ exhibits the highest
ATTR, whereas the proposed algorithm demonstrates the lowest ATTR. This indicates that the
proposed algorithm requires the shortest average time to achieve a rendezvous. Therefore,
attempting rendezvous using the proposed algorithm can lead to faster average rendezvous times
compared to other algorithms. This implies a potential reduction in the amount of battery
consumed by CRN nodes during communication.
Figure 4. ATTR

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Figure 5. Rendezvous Attempt Count
Figure 5 displays the number of attempts required to achieve a rendezvous. The Rendezvous
Attempt Count represents the number of times two communicating nodes try to find each other
on a common channel to initiate communication. This metric signifies the average number of
attempts needed for nodes within a wireless communication network to locate each other and
begin communication, serving as a crucial indicator of the performance of rendezvous
algorithms. An efficient rendezvous algorithm aims to minimize this number, facilitating nodes to
quickly discover each other and commence communication. As shown in Figure 5, the proposed
algorithm exhibits the lowest number of rendezvous attempts. This indicates that using the
proposed algorithm can reduce the number of attempts needed for CRN nodes to achieve a
rendezvous, thereby potentially decreasing the battery consumption of CRN nodes.
The proposed algorithm demonstrated superior performance in terms of MTTR, ATTR, and
Rendezvous Attempt Count compared to other algorithms, showcasing its strengths. The
significance of these results lies in the ability of CRN nodes attempting rendezvous to achieve
success more rapidly than with other algorithms. This outcome also suggests the potential for
conserving battery life in the nodes attempting rendezvous, which stands as another advantage.
However, this paper did not conduct experiments on actual battery usage, which remains an area
for future research.
The proposed algorithm reduces the time required for rendezvous and ensures successful
rendezvous within the suggested sequence length. When applied in actual post-disaster scenario
CRN environments, this algorithm is expected to enhance the reliability of network
configurations. Furthermore, it can be utilized as a method for establishing efficient networks.
5. CONCLUSION
In this study, we introduce a novel rendezvous sequence generation algorithm aimed at swiftly
establishing networks in post-disaster scenarios following an event. The sequences generated by
the proposed algorithm are utilized by CRN nodes for channel visits during the rendezvous
process. Following the sequence, CRN nodes systematically visit channels. Through simulations,
we compared the proposed algorithm with existing algorithms and observed that the generated
sequences outperform those of existing rendezvous sequence generation algorithms in terms of

27
MTTR and ATTR. We also evaluated the number of rendezvous attempts. A decrease in attempt
count signifies fewer communication attempts, indicating that the proposed algorithm is more
efficient at preserving the battery life of CRN nodes deployed in post-disaster scenarios
compared to other algorithms. This efficiency is attributed to the fact that our algorithm requires
fewer channel visits to achieve a rendezvous, thereby reducing the energy consumed during
channel visits.
The proposed algorithm accelerates the rendezvous process in post-disaster scenarios; however,
numerous considerations, such as the quality and reliability of channel connections, must be
further addressed. There is a need for additional research to discuss how these areas can be
enhanced.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
This work was supported by Yeungnam University College Research Grants in 2020.
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AUTHOR
Dr. Junhyung Kim received his Ph.D. degree in Computer Science and Engineering
from Kyungpook National University, Daegu, Korea, in 2015. He worked as an
Assistant Manager for Techbile Co.Ltd. from 2004 to 2007, Daegu, Korea.
Subsequently, he also worked as a Deputy General Manager at Geo Consulting &
Information Co., Ltd. from 2015 to 2016. Since 2016, he has been an Assistant
Professor at the Yeungnam University College of Science and Technology, Daegu,
Korea. His research interests include computer networks, cognitive radio networks, and
wireless sensor networks.

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Rendezvous Sequence Generation Algorithm for Cognitive Radio Networks in Post-Disaster Scenario