Network Link Status Research Articles

Design of Smart Home System based on Wireless Sensor Network Link Status Awareness Algorithm

When wireless sensor networks are used in smart homes, the connection state will be unstable due to signal masking attenuation. This will cause low packet rate, high time delay and high cost in the network. In this paper, a network routing algorithm for wireless sensing based on connection conditions is designed. Secondly, the expected number of sends is proposed to evaluate the stability of links. Based on this, the following network signal delivery situation is forecasted in real time and quickly. According to the estimated expected number of transmissions, the path is dynamically corrected to effectively avoid attenuation in the channel and achieve optimal system performance. Experimental results show that the method proposed in this paper can improve the efficiency of message sending and reduce the routing cost under the condition of masking effect.

A novel hierarchical distributed vehicular edge computing framework for supporting intelligent driving

Recently, various infrastructure-assisted or onboard driving assistant applications have been proposed as a component of intelligent transportation systems (ITS) to improve the transportation system’s efficiency and release public concern about road safety. However, such AI-assisted intelligent applications are mainly data-driven and put great demands on the computing power of the ITS systems. Therefore, in the highly dynamic Internet-of-Vehicles environment in ITS, how to effectively coordinate the limited computing power of the various components of the system and realize reliable support for such resource-consuming applications through efficient resource allocation methods is the focus of our research. Accordingly, a novel joint computing and communication resource scheduling method is proposed to fulfill those ITS applications’ inherent heterogeneous quality of service (QoS) requirements. By fully exploiting the computing resources provided by the onboard computing device, the edge computing device located in the vehicle’s proximity and remote data center, we designed a hierarchical three-layer Vehicular Edge Computing (VEC) framework. Briefly, an onboard joint computation offloading and transmission scheduling policy is designed to assign corresponding offloading decisions to the locally generated computing tasks by considering the vehicle’s computing resources and real-time network link status. Additionally, a new distributed resource allocation policy is developed for the edge devices, in which we derive a server selection policy and allocate communication time based on our proposed metric. To evaluate the performance and validate the effectiveness of our proposed method, we conduct extensive simulation tests and ablation experiments, respectively. The results show that our approach can achieve stable performance in various experimental settings. Also, compared to the state-of-the-art algorithms, our joint resource allocation policy significantly reduces the scheduling overhead, improves the utilization of system resources, and minimizes the data transmission delay caused by vehicle motion.

Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks.

Fast convergence routing is a critical issue for Low Earth Orbit (LEO) constellation networks because these networks have dynamic topology changes, and transmission requirements can vary over time. However, most of the previous research has focused on the Open Shortest Path First (OSPF) routing algorithm, which is not well-suited to handle the frequent changes in the link state of the LEO satellite network. In this regard, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, where the satellite can quickly obtain the network link status and adjust its routing strategy accordingly. In FRL-SR, each satellite node is considered an agent, and the agent selects the appropriate port for packet forwarding based on its routing policy. Whenever the satellite network state changes, the agent sends "hello" packets to the neighboring nodes to update their routing policy. Compared to traditional reinforcement learning algorithms, FRL-SR can perceive network information faster and converge faster. Additionally, FRL-SR can mask the dynamics of the satellite network topology and adaptively adjust the forwarding strategy based on the link state. The experimental results demonstrate that the proposed FRL-SR algorithm outperforms the Dijkstra algorithm in the performance of average delay, packet arriving ratio, and network load balance.

A New Method for Reconstructing Data Considering the Factor of Selected Provider Nodes Set in Distributed Storage System

In the distributed storage system, when data need to be recovered after node failure, the erasure code redundancy method occupies less storage space than the multi-copy method. At present, the repair mechanism using erasure code to reconstruct the failed node only considers the improvement of link bandwidth on the repair rate and does not consider the impact of the selection of data providing node-set on the repair performance. A single node fault data reconstruction method based on the Software Defined Network (SDN) using the erasure code method is designed to solve the above problems. This method collects the network link-state through SDN, establishes a multi-attribute decision-making model of the data providing node-set based on the node performance, and determines the data providing nodes participating in providing data through the ideal point method. Then, the data recovery problem of a single fault node is modeled as the optimization problem of an optimal repair tree, and a hybrid genetic algorithm is designed to solve it. The experimental results show that under the same erasure code scale, after selecting the nodes of the data providing node-set, compared with the traditional tree topology and star topology, the repair delay distribution of the designed single fault node repair method for a distributed storage system is reduced by 15% and 45% respectively, and the repair flow is close to the star topology, which is reduced by 40% compared with the traditional tree repair.

ALBLP: Adaptive Load-Balancing Architecture Based on Link-State Prediction in Software-Defined Networking

Load-balancing optimization in software-defined networking (SDN) has been researched for a long time. Researchers have proposed many solutions to the load-balancing problem but have rarely considered the impact of transmission delay between controllers and switches under high-load network conditions. In this paper, we propose an adaptive load-balancing architecture based on link-state prediction (ALBLP) in SDN that can solve the influence of transmission delay between controllers and switches on network load balancing. ALBLP constructs the prediction model of the network link status, adopts the long-term and short-term memory neural network (LSTM) algorithm to predict the network link-state value, and then uses the predicted value as the Dijkstra weight to calculate the optimal path between network hosts. The proposed architecture can adaptively optimize network load balancing and avoid the empty window period, in which the switch flow table does not exist by actively issuing the flow table. Under the network architecture, we collect the data set of the network link-state by simulating the GÉANT network, and we verify the effectiveness of the proposed algorithm. The experiment results show that the ALBLP proposed in this paper can optimize load balancing in SDN and solve the problem of transmission delay between controllers and switches. It has a maximum load-balancing improvement of 23.7% and 11.7% in comparison with the traditional Open Shortest Path First (OSPF) algorithm and the reinforcement learning method based on Q-Learning.

Enhancing network link state estimation by incorporating passive packet pair measurement into the Mockets transport protocol

Thanks to their limited network footprint, passive approaches to bandwidth estimation are particularly interesting when applied to resource constrained network environments such as tactical networks. Regrettably, passive approaches are typically afflicted by lower accuracy when compared to active bandwidth detection techniques that operate by saturating the pipe or otherwise injecting probing traffic. To overcome this limitation, we designed Passive Packet Pair (P3), a passive version of the packet pair bandwidth estimation mechanism. P3 assesses the bandwidth available over the end-to-end path by measuring differences in the inter-arrival times of packet pairs or trains produced at the transport protocol level starting from application-generated traffic. This paper presents P3 and discusses its integration with Mockets, a communications middleware that provides transport protocol capabilities to applications running in tactical networks, and SENSEI, a passive monitoring and network awareness platform. Finally, the article discusses the results of experiments run in a virtual environment with the goal of evaluating the accuracy of P3 under different system conditions.

Reinforcement Learning Based Mobility Adaptive Routing for Vehicular Ad-Hoc Networks

Vehicular ad-hoc networks (VANETs) is drawing more and more attentions in intelligent transportation system to reduce road accidents and assist safe driving. However, due to the high mobility and uneven distribution of vehicles in VANETs, multi-hops communication between the vehicles is still particularly challenging. Considering the distinctive characteristics of VANETs, in this paper, an adaptive routing protocol based on reinforcement learning (ARPRL) is proposed. Through distributed Q-Learning algorithm, ARPRL constantly learns and obtains the fresh network link status proactively with the periodic HELLO packets in the form of Q table update. Therefore, ARPRL's dynamic adaptability to network changes is improved. Novel Q value update functions which take into account the vehicle mobility related information are designed to reinforce the Q values of wireless links by exchange of HELLO packets between neighbor vehicles. In order to avoid the routing loops caused in Q learning process, the HELLO packet structure is redesigned. In addition, reactive routing probe strategy is applied in the process of learning to speed up the convergence of Q learning. Finally, the feedback from the MAC layer is used to further improve the adaptation of Q learning to the VANETs environment. Through simulation experiment result, we show that ARPRL performs better than existing protocols in the form of average packet delivery ratio, end-to-end delay and number hops of route path while network overhead remains within acceptable ranges.

Self-match based on polling scheme for passive optical network monitoring

We propose a self-match based on polling scheme for passive optical network monitoring. Each end-user is equipped with an optical matcher that exploits only the specific length patchcord and two different fiber Bragg gratings with 100% reflectivity. The simple and low-cost scheme can greatly simplify the final recognition processing of the network link status and reduce the sensitivity of the photodetector. We analyze the time-domain relation between reflected pulses and establish the calculation model to evaluate the false alarm rate. The feasibility of the proposed scheme and the validity of the time-domain relation analysis are experimentally demonstrated.

Robust Transmission of Scalable Video Coding Bitstream Over Heterogeneous Networks

Video transmission using Scalable Video Coding (SVC) enables the functionalities of adaptation for both channel bandwidth and terminal device. However, link adaptation, which requires the video bitstream to provide error resilient scalability (ERS), is currently not supported in SVC. In this paper, we propose a robust SVC bitstream transmission framework, which integrates both error resilient (ER) coding and error concealment (EC) to enable the desired link adaptability. First, at the encoder, we design an ER coding scheme for SVC using redundant pictures, in which redundant picture information (RPI) is generated under rate-distortion criteria and transmitted to the media gateway together with the SVC bitstream. Then, ERS can be effectively achieved at the media gateway by selectively adding or removing coded pictures of different video coding layers according to the RPI and current network link status. Finally, at the decoder, we take advantage of the unique characteristics of SVC to develop an EC scheme based on both the proposed Virtual-BLSkip method and Wiener filtering to recover the lost or removed enhancement layer pictures. The proposed framework has negligible impact on the coding efficiency because it transmits RPIs rather than directly adding redundancy into the original bitstream. More importantly, it successfully strikes a balance between coding efficiency, error resiliency, and operation complexity. It is capable of providing the desired ERS with extremely low complexity, and maintaining virtually the same bit rate as input video bitstream. The experimental results show that the proposed framework achieves significant performance gains over existing ER transmission schemes.

네트워크 기상 관리 시스템의 설계 및 구현

본 논문에서는 네트워크의 전반적인 구조와 함께 트래픽 흐름을 한 눈에 보여줄 수 있는 거시적인 view를 제공하는 네트워크 기상도 시스템을 위한 모델을 제안하고, 이를 설계 및 구현 하여 실제 전국 규모의 연구망에 구축 및 활용된 사례를 소개한다. 개발된 기상도 시스템은 전반적인 네트워크 사용 현황 정보에 더해 네트워크 내의 주요 라우터 노드 및 백본 링크들에 대한 현 상황은 물론 과거의 주요 트래픽 플로우 상황 정보들을 모두 데이터베이스화하고, 이렇게 축적된 데이터를 바탕으로 웹 기반의 질의 응답 기능까지 모두 제공하는 통합 관리 시스템으로, 저비용으로 효율적인 네트워크 관리 시스템을 필요로 하는 중소규모 망 사업자 및 관리자들에게 하나의 좋은 관리 도구가 될 수 있을 것이다. In this paper, we design and implement a network weather map system, which provides a macroscopic view on the whole network topology as well as the network link status and utilization. The proposed system also provides distributed NetFlow-based database facility and Web-based query interface, through which network operators can check the detailed network router or link status as well as submit predefined queries to easily find out and locate heavy hitters and/or their usage. We believe that our develop system will be a useful tool for small-to-mid-scale ISPs or network operators, in managing their own networks in a cost-effective way.