193 research outputs found
The Language Learning of Refugee Students in Canadian Public Elementary and Secondary Schools
This dissertation investigated the role that funds of knowledge play in refugee students’ language acquisition and acculturation to the new school, and the support schools provided for their language learning.
This study explores theoretical frameworks related to student ecological environments, second language acquisition, and funds of knowledge that refugee students bring with them to the classroom. This qualitative research study presents a case study of four refugee students in both ESL classrooms and mainstream classrooms at an elementary school and a secondary school. Refugee students’ English language learning was scrutinized through research techniques which include participant observation, interviews, and documents. Through the profiles of the four students, this study further highlights the lived experiences of students related to their English language learning. Major findings include: 1) the need to prepare teachers more fully, such as drawing on funds of knowledge and develop a deeper understanding of the challenges and needs that refugee students face; 2) the need to enhance communication and cooperation between systems around refugee students; 3) the need to provide both linguistic and social support. This study concludes with reflections on current ESL practices for refugee students and recommendations for teaching refugee students
A Survey of Network Protocol Fuzzing: Model, Techniques and Directions
As one of the most successful and effective software testing techniques in
recent years, fuzz testing has uncovered numerous bugs and vulnerabilities in
modern software, including network protocol software. In contrast to other
fuzzing targets, network protocol software exhibits its distinct
characteristics and challenges, introducing a plethora of research questions
that need to be addressed in the design and implementation of network protocol
fuzzers. While some research work has evaluated and systematized the knowledge
of general fuzzing techniques at a high level, there is a lack of similar
analysis and summarization for fuzzing research specific to network protocols.
This paper offers a comprehensive exposition of network protocol software's
fuzzing-related features and conducts a systematic review of some
representative advancements in network protocol fuzzing since its inception. We
summarize state-of-the-art strategies and solutions in various aspects, propose
a unified protocol fuzzing process model, and introduce the techniques involved
in each stage of the model. At the same time, this paper also summarizes the
promising research directions in the landscape of protocol fuzzing to foster
exploration within the community for more efficient and intelligent modern
network protocol fuzzing techniques
User-Defined Privacy Location-Sharing System in Mobile Online Social Networks
With the fusion of social networks and location-based services, location sharing is one of the most important services in mobile online social networks (mOSNs). In location-sharing services, users have to provide their location information to service provider. However, location information is sensitive to users, which may cause a privacy-preserving issue needs to be solved. In the existing research, location-sharing services, such as friends’ query, does not consider the attacks from friends. In fact, a user may not trust all of his/her friends, so just a part of his/her friends will be allowed to obtain the user’s location information. In addition, users’ location privacy and social network privacy should be guaranteed. In order to solve the above problems, we propose a new architecture and a new scheme called User-Defined Privacy Location-Sharing (UDPLS) system for mOSNs. In our scheme, the query time is almost irrelevant to the number of friends. We also evaluate the performance and validate the correctness of our proposed algorithm through extensive simulations
Performance Analysis of Non-ideal Wireless PBFT Networks with mmWave and Terahertz Signals
Due to advantages in security and privacy, blockchain is considered a key
enabling technology to support 6G communications. Practical Byzantine Fault
Tolerance (PBFT) is seen as the most applicable consensus mechanism in
blockchain-enabled wireless networks. However, previous studies on PBFT do not
consider the channel performance of the physical layer, such as path loss and
channel fading, resulting in research results that are far from real networks.
Additionally, 6G communications will widely deploy high frequency signals such
as millimeter wave (mmWave) and terahertz (THz), while the performance of PBFT
is still unknown when these signals are transmitted in wireless PBFT networks.
Therefore, it is urgent to study the performance of non-ideal wireless PBFT
networks with mmWave and THz siganls, so as to better make PBFT play a role in
6G era. In this paper, we study and compare the performance of mmWave and THz
signals in non-ideal wireless PBFT networks, considering Rayleigh Fading (RF)
and close-in Free Space (FS) reference distance path loss. Performance is
evaluated by consensus success rate and delay. Meanwhile, we find and derive
that there is a maximum distance between two nodes that can make PBFT consensus
inevitably successful, and it is named active distance of PBFT in this paper.
The research results not only analyze the performance of non-ideal wireless
PBFT networks, but also provide an important reference for the future
transmission of mmWave and THz signals in PBFT networks.Comment: IEEE International Conference on Metaverse Computing, Networking and
Applications (MetaCom) 202
ESCM: An Efficient and Secure Communication Mechanism for UAV Networks
UAV (unmanned aerial vehicle) is gradually entering various human activities.
It has also become an important part of satellite-air-ground-sea integrated
network (SAGS) for 6G communication. In order to achieve high mobility, UAV has
strict requirements on communication latency, and it cannot be illegally
controlled as weapons of attack with malicious intentions. Therefore, an
efficient and secure communication method specifically designed for UAV network
is required. This paper proposes a communication mechanism named ESCM for the
above requirements. For high efficiency of communication, ESCM designs a
routing protocol based on artificial bee colony algorithm (ABC) for UAV network
to accelerate communication between UAVs. Meanwhile, we plan to use blockchain
to guarantee the communication security of UAV networks. However, blockchain
has unstable links in high mobility network scenarios, resulting in low
consensus efficiency and high communication overhead. Therefore, ESCM also
introduces the concept of the digital twin, mapping the UAVs from the physical
world into Cyberspace, transforming the UAV network into a static network. And
this virtual UAV network is called CyberUAV. Then, in CyberUAV, we design a
blockchain system and propose a consensus algorithm based on network coding,
named proof of network coding (PoNC). PoNC not only ensures the security of
ESCM, but also further improves the performance of ESCM through network coding.
Simulation results show that ESCM has obvious advantages in communication
efficiency and security. Moreover, encoding messages through PoNC consensus can
increase the network throughput, and make mobile blockchain static through
digital twin can improve the consensus success rate
Performance Analysis and Comparison of Non-ideal Wireless PBFT and RAFT Consensus Networks in 6G Communications
Due to advantages in security and privacy, blockchain is considered a key
enabling technology to support 6G communications. Practical Byzantine Fault
Tolerance (PBFT) and RAFT are seen as the most applicable consensus mechanisms
(CMs) in blockchain-enabled wireless networks. However, previous studies on
PBFT and RAFT rarely consider the channel performance of the physical layer,
such as path loss and channel fading, resulting in research results that are
far from real networks. Additionally, 6G communications will widely deploy
high-frequency signals such as terahertz (THz) and millimeter wave (mmWave),
while performances of PBFT and RAFT are still unknown when these signals are
transmitted in wireless PBFT or RAFT networks. Therefore, it is urgent to study
the performance of non-ideal wireless PBFT and RAFT networks with THz and
mmWave signals, to better make PBFT and RAFT play a role in the 6G era. In this
paper, we study and compare the performance of THz and mmWave signals in
non-ideal wireless PBFT and RAFT networks, considering Rayleigh Fading (RF) and
close-in Free Space (FS) reference distance path loss. Performance is evaluated
by five metrics: consensus success rate, latency, throughput, reliability gain,
and energy consumption. Meanwhile, we find and derive that there is a maximum
distance between two nodes that can make CMs inevitably successful, and it is
named the active distance of CMs. The research results not only analyze the
performance of non-ideal wireless PBFT and RAFT networks, but also provide
important references for the future transmission of THz and mmWave signals in
PBFT and RAFT networks.Comment: arXiv admin note: substantial text overlap with arXiv:2303.1575
A Q-learning-based approach for deploying dynamic service function chains
As the size and service requirements of today’s networks gradually increase, large numbers of proprietary devices are deployed, which leads to network complexity, information security crises and makes network service and service provider management increasingly difficult. Network function virtualization (NFV) technology is one solution to this problem. NFV separates network functions from hardware and deploys them as software on a common server. NFV can be used to improve service flexibility and isolate the services provided for each user, thus guaranteeing the security of user data. Therefore, the use of NFV technology includes many problems worth studying. For example, when there is a free choice of network path, one problem is how to choose a service function chain (SFC) that both meets the requirements and offers the service provider maximum profit. Most existing solutions are heuristic algorithms with high time efficiency, or integer linear programming (ILP) algorithms with high accuracy. It’s necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. In this paper, we propose the Q-learning Framework Hybrid Module algorithm (QLFHM), which includes reinforcement learning to solve this SFC deployment problem in dynamic networks. The reinforcement learning module in QLFHM is responsible for the output of alternative paths, while the load balancing module in QLFHM is responsible for picking the optimal solution from them. The results of a comparison simulation experiment on a dynamic network topology show that the proposed algorithm can output the approximate optimal solution in a relatively short time while also considering the network load balance. Thus, it achieves the goal of maximizing the benefit to the service provider
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