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

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

Low-latency and Resource-efficient Service Function Chaining Orchestration in Network Function Virtualization

© 2014 IEEE. Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing