High Packet Loss Rate Research Articles

Flexible Virtual Cell Design for Ultradense Networks: A Machine Learning Approach

With the ever-growing demand for even higher throughput, ultradense networks (UDNs) are being deployed for the fifth generation (5G) mobile communications. Although massively distributed radio access points (APs) result in a considerable increase in throughput, they also cause some critical problems. When employing a wireless backhaul, the backhaul capacity becomes a limiting factor, which may result in a high packet loss rate. Furthermore, dense deployment of APs leads to more frequent handoffs for mobile user equipments, which results in heavy measurement and signaling overhead. To address the problem of frequent handoffs, virtual cell (VC) has been considered as a promising solution. However, the limited wireless backhaul capacity encountered by inflexible VC design may still result in an intolerable packet loss rate. For a better trade-off between the packet loss rate and the handoff overhead, a machine learning approach for flexible VC design is proposed that leverages particle swarm optimization (PSO) to quickly find the optimal VC solution. To be responsive to the dynamic traffic demand and backhaul capacity of APs, a new parameter called “weighted distance” is employed in the modified K-means algorithm, which is nested in the PSO procedures for master AP selection and VC boundary determination. Compared with an exhaustive search, optimal VC solutions can be found efficiently through considerably fewer iterations. The proposed method is generic and applicable to disparate UDN application scenarios.

RETRACTED: Design and research on wireless intelligent monitoring system for sewage pipeline leakage of textile mill

The traditional textile factory sewage pipeline leakage detection system has many defects, such as high packet loss rate, low accuracy and low system alarm response rate. This paper designs a new wireless intelligent monitoring system for sewage pipeline leakage of textile mill. Firstly, the DSP chip, analog-to-digital conversion circuit, MCU, LCD interface module and communication module were designed. After the design of each module, the efficient processing and spectrum analysis of water leakage acoustic digital signal were achieved, the synchronous acquisition of detection instrument data was realized, thus improving the operation frequency of the system. Secondly, the pipeline leakage location function was designed. Through the power spectrum, the leakage sound wave spectrum was analyzed to obtain the important characteristics of the leakage sound wave, and then the leakage detection was implemented. Thus, achieving the textile factory sewage pipeline leakage wireless intelligent monitoring. The results show that the system designed in this work reduces the monitoring packet loss rate, the alarm response time and false alarm rate, and improves the monitoring accuracy.

EM-RPL: Enhanced RPL for Multigateway Internet-of-Things Environments

The IPv6 routing protocol for low power and lossy networks (RPL) has some shortcomings, such as high packet loss rate and low network lifetime when used in Internet-of-Things (IoT) environments under heavy traffic. To overcome the RPL limitations, the current research tends to focus on a new paradigm of routing, referred to as Anycast Routing, in which a source node targets a set of destinations rather than a single one. In this article, we present a protocol that exploits the anycast perspective in the routing process since an important aspect of most IoT environments is to forward packets to a gateway, no matter which. Besides, we interconnect various instances of RPL to achieve better routing performance by offering the possibility of cooperation among various simultaneous instances of RPL within the network. Finally, to reach higher performance, we use a rank computation and parent selection mechanism that is different from those of the RPL. The evaluation results, which are obtained through simulation with the Cooja simulator, show that EM-RPL outperforms RPL in reducing the environmental footprint of the network, while it extends the network lifetime, decreases packet loss ratio, and better controls interpacket intervals and parent change overhead.

Jamming-Resilient Backup Nodes Selection for RPL-based Routing in Smart Grid AMI Networks

Advanced metering infrastructure (AMI) is the core component of the smart grid. As the wireless connection between smart meters in AMI is featured with high packet loss and low transmission rate, AMI is considered as a representative of the low power and lossy networks (LLNs). In such communication environment, the routing protocol in AMI network is essential to ensure the reliability and real-time of data transmission. The IPv6 routing protocol for low-power and lossy networks (RPL), proposed by IETF ROLL working group, is considered to be the best routing solution for the AMI communication environment. However, the performance of RPL can be seriously degraded due to jamming attack. In this paper, we analyze the performance degradation problem of RPL protocol under jamming attack. We propose a backup node selection mechanism based on the standard RPL protocol. The proposed mechanism chooses a predefined number of backup nodes that maximize the probability of successful transmission. We evaluation the proposed mechanism through MATLAB simulations, results show the proposed mechanism improves the performance of RPL under jamming attack prominently.

Design of data transmission system for 3D laser scanning of liquefied gas railway tanker based on fuzzy algorithm

Aiming at the problem of high packet loss rate in the traditional 3D laser scanning data transmission system, a 3D laser scanning data transmission system based on fuzzy algorithm is designed. The operational amplifier is used as the analog front-end

Detection and Mitigation of DoS Attacks in Software Defined Networks

The introduction of software-defined networking (SDN) has emerged as a new network paradigm for network innovations. By decoupling the control plane from the data plane in traditional networks, SDN provides high programmability to control and manage networks. However, the communication between the two planes can be a bottleneck of the whole network. SDN-aimed DoS attacks can cause long packet delay and high packet loss rate by using massive table-miss packets to jam links between the two planes. To detect and mitigate SDN-aimed DoS attacks, this paper presents FloodDefender, an efficient and protocol-independent defense framework for SDN/OpenFlow networks. FloodDefender stands between the controller platform and other controller apps, and conforms to the OpenFlow policy without additional devices. The detection module in FloodDefender utilizes new frequency features to precisely identify SDN-aimed DoS attacks. The mitigation module uses three new techniques to efficiently mitigate attack traffic: table-miss engineering to prevent the communication bandwidth from being exhausted; packet filter to filter out attack traffic and save computational resources of the control plane; and flow rule management to eliminate most of useless flow entries in the switch flow table. Our evaluation on a prototype implementation of FloodDefender shows that the defense framework can precisely identify and efficiently mitigate the SDN-aimed DoS attacks with very little overhead.

Network Coding for Efficient Video Multicast in Device-to-Device Communications.

Device-to-Device (D2D) communication is one of the critical technologies for the fifth-generation network, which allows devices to communicate directly with each other while increasing transmission rate, but this communication is vulnerable to interference. When video transmission is carried out in an environment with interference, problems such as high packet loss rate, poor quality of the video, and blurred screen may exist. These problems can be effectively solved by redundant coding operations at the source node, but the extra coding operation imposes a heavy computational burden on the source node. In order to alleviate the computational overhead of the source node, reduce transmission delay, and guarantee transmission quality, this paper proposes an efficient video multicast transmission scheme based on Random Linear Network Coding (RLNC) in D2D networks. In the scheme, the receiving devices in the transmission participate in the process of generating repair packets that are used to remedy the loss of encoded packets during transmission. The source node multicasts the encoded video file. The receiving nodes re-encode the received data packets with RLNC and then send them to the network again. The nearby nodes can decode the original data through the encoded or re-encoded data packets. The performance of the proposed scheme is evaluated through both simulation and real experiments. The experimental results show that compared with the traditional RLNC scheme, this scheme could balance the computation overhead of the mobile devices and reduce the encoding and decoding delay by about 8%. When the packet loss rate is high, the proposed scheme can obtain better video quality than the traditional replication-based scheme.

Enhanced exponential rule scheduling algorithm for real-time traffic in LTE network

Nowadays, mobile communication is growing rapidly and become an everyday commodity. The vast deployment of real-time services in Long Term Evolution (LTE) network demands for the scheduling techniques that support the Quality of Service (QoS) requirements. LTE is designed and implemented to fulfill the users’ QoS. However, 3GPP does not define the specific scheduling technique for resource distribution which leads to vast research and development of the scheduling techniques. In this context, a review of the recent scheduling algorithm is reported in the literature. These schedulers in the literature cause high Packet Loss Rate (PLR), low fairness, and high delay. To cope with these disadvantages, we propose an enhanced EXPRULE (eEXPRULE) scheduler to improve the radio resource utilization in the LTE network. Extensive simulation works are carried out and the proposed scheduler provides a significant performance improvement for video application without sacrificing the VoIP performance. The eEXPRULE scheduler increases video throughput, spectrum efficiency, and fairness by 50%, 13%, and 11%, respectively, and reduces the video PLR by 11%.

Multi-channel scheduling analysis of dynamic data in wireless networks oriented big data

In order to reduce the running time of dynamic data multi-channel scheduling in wireless networks, a multi-channel scheduling analysis method based on partially observable Markov decision process (POMDP) for dynamic data of wireless networks is proposed. Firstly, the data type and dynamic data transmission process in wireless network are analysed. According to the node's request arrival rate and service rate, the network state transition probability and observation probability are calculated. Load balancing is used as the performance optimisation target of dynamic data multi-channel in wireless network, calculate its performance function. By calculating the observation probability and performance function, the dynamic data multi-channel scheduling analysis for big data wireless networks is finally realised. The experimental results show that the proposed method has high data transmission efficiency and low packet loss rate, and the scheduling operation time is relatively short. The effectiveness of the proposed method is verified.

Multipath-Based HTTP Adaptive Streaming Scheme for the 5G Network

Fifth-generation (5G) communication has a non-line-of-sight (non-LOS) property, in that it cannot pass through obstacles due to the characteristics of ultra-high frequencies. This can lead to a high packet loss rate, which results in loss-based congestion. As the demand for high-quality video streaming services increases, multipath-based Hypertext Transfer Protocol (HTTP) adaptive streaming has been widely studied. It has the advantage of improving stability using bandwidth aggregation and alternative transmission. The request policies of existing systems degrade bandwidth utilization by the ON-OFF traffic pattern and degrade the quality-of-experience (QoE) due to incorrect bandwidth estimation. In the 5G environment, the above problems are exacerbated by severe network asymmetry, and buffer underflow occurs due to the drastic bandwidth changes in a non-LOS environment. We propose a multipath-based transmission scheme to solve the problem of HTTP adaptive streaming in a 5G environment. The proposed scheme presents a multipath-based collective segment request policy for accurate aggregated bandwidth estimation and bandwidth utilization improvement. The aggregated bandwidth estimation measures both block-based and segment-based bandwidth estimation values to improve network responsiveness and stability. We propose a rate adaptation scheme to improve bandwidth utilization and QoE. We also present a segment scheduler to solve the out-of-order problem in multipath-based transmission, and propose an offloading control scheme based on a partial-segment-based request policy to prevent buffer underflow due to sudden bandwidth reduction in the non-LOS 5G environment. Quantitative results from simulations suggest that our scheme solves the shortcomings of the existing solutions improving both bandwidth utilization and QoE.

An Efficient Dynamic Load Balancing Scheme Based on Nash Bargaining in SDN

Static multi-controller deployment architecture cannot adapt to the drastic changes of network traffic, which will lead to a load imbalance between controllers, resulting in a high packet loss rate, high latency, and other network performance degradation problems. In this paper, an efficient dynamic load balancing scheme based on Nash bargaining is proposed for a distributed software-defined network. Firstly, considering the connectivity of network nodes, the switch migration problem is transformed into a network mapping relationship reconstruction problem. Then, we establish the Nash bargaining game model to fairly optimize the two contradictory goals of migration cost and load balance. Finally, the model is solved by an improved firefly algorithm, and the optimal network mapping state is obtained. The experimental results show that this scheme can optimize the migration cost and load balance at the same time. Compared with the existing research schemes, the migration process of the switch is optimized, and, while effectively balancing the load of the control plane, the migration cost is reduced by 14.5%.

Low delay error resilience algorithm for H.265|HEVC video transmission

Transmission of high-resolution compressed video on unreliable transmission channels with time-varying characteristics such as wireless channels can adversely affect the decoded visual quality at the decoder side. This task becomes more challenging when the video codec computational complexity is an essential factor for low delay video transmission. High-efficiency video coding (H.265|HEVC) standard is the most recent video coding standard produced by ITU-T and ISO/IEC organisations. In this paper, a robust error resilience algorithm is proposed to reduce the impact of erroneous H.265|HEVC bitstream on the perceptual video quality at the decoder side. The proposed work takes into consideration the compatibility of the algorithm implementations with and without feedback channel update. The proposed work identifies and locates the frame’s most sensitive areas to errors and encodes them in intra mode. The intra-refresh map is generated at the encoder by utilising a grey projection method. The conducted experimental work includes testing the codec performance with the proposed work in error-free and error-prone conditions. The simulation results demonstrate that the proposed algorithm works effectively at high packet loss rates. These results come at the cost of a slight increase in the encoding bit rate overhead and computational processing time compared with the default HEVC HM16 reference software.

The improvement of node Mobility in RPL to increase transmission efficiency

The Internet of Thing has gained interested to use for daily devices to industrial applications. Mission-critical applications such as connected car and healthcare services require real-time communications and mobility support. The 6LoWPAN protocol and IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) have become the standard for the IoT. However, the RPL protocol is unable to support the application requirement causing from the high network overhead, long message latency and high packet loss rate due to mobility. Thus, in this paper, we propose a new cost metric combining the number of hops, RSSI values, and the summation of delay to enhance RPL mobility. In addition, we define the movement notification for the mobile node to activate mobile detection and parent selection processes. Finally, we presented a comparative study of the improved RPL protocols in terms of packet delivery ratio, end-to-end delay and the number of control messages. The result shows that improved RPL protocol with the new cost metrics provides a high packet delivery ratio and offers a low message latency.

Multi-path data fusion method based on routing algorithm for wireless sensor networks

ABSTRACT For the purpose of saving the resources of wireless sensor networks, it is necessary to fuse the existing data in wireless sensor networks. When using the current method to fuse data in wireless sensor networks, the fusing time is too long to remove the noise signal in wireless sensor networks effectively. In the process of fusing, data packet loss is easy to occur. There are some problems such as low fusion efficiency, poor denoising performance and high packet loss rate. A multi-path data hierarchical fusion method based on routing algorithm is proposed in this paper. The redundant node decision method based on mesh partitioning is combined with the grid algorithm and EECRS protocol to establish a routing protocol based on redundant node elimination, and the redundant data existing in the redundant nodes of the wireless sensor network is removed. Based on the statistical data of the data, the probability density function is deduced and analyzed, and the noise filtering matrix is constructed to optimize the measurement matrix and remove the noise existing in the wireless sensor network. The multi-path scheduling technology is used to synthesize the pheromone table and the data forwarding flow table, and the ant is used to actively monitor the link QOS real-time information in the path finding process to update the pheromone table and the flow table, and calculate the link idle rate according to the update result. According to the calculation result, the data transmission path is selected, and the multi-path data layer fusion is completed. The experimental results show that the proposed method has high fusion efficiency, good denoising performance and low packet loss rate.

A DTN Routing Scheme for LEO Satellites Topology

In delay/disruption-tolerant satellite networks, the existing routing algorithms have problems of long transmission delay, high packet loss rate, excessive network overhead and other issues. In the view of these shortcomings, an improved contact graph routing algorithm, ICGR, is proposed in this paper. Firstly, the impact of queuing delay is considered by defining a queue factor and proposing a queue scheduling mechanism in routing calculation; secondly, the effects of contact’s remaining capacity are taken into account to avoid the selected path being unable to forward the messages; thirdly, routing update mechanism is optimized by designing a message path update factor to strike a balance between real-time updates of transmission and resource consumption; finally, a LEO satellite network simulation platform is built with OPNET to verify the performance of this algorithm. Simulation results show that the ICGR, compared with the ED and CGR algorithm, has advantage in packet delivery rate, average transmission delay and network overhead.

DCSN-Cast: Deep compressed sensing network for wireless video multicast

The purpose of wireless video multicast is to send a video signal simultaneously to multiple heterogeneous users, each of whom desires video quality that matches its channel condition. The current compressed sensing (CS)-based wireless video multicast has some advantages but also some shortcomings, such as high computational and low reconstruction quality, especially at high packet loss rates. This paper aims to improve the current CS-based multicasts, and proposes two deep compressed sensing networks for wireless video multicast, abbreviated as DCSN-Cast. We first consider a residual DCSN-Cast (DCSRN-Cast), which consists of two parts: a fully connected network and a deep residual network. The fully connected network takes the measurements of CS as input and outputs the preliminary reconstructed image, while the deep residual network takes this preliminary reconstructed image and outputs the final reconstructed image. The second scheme is a fully connected deep neural network (DCSFCN-Cast) which prunes the convolutional neural network under consideration to reduce the complexity. Extensive experiments show that the proposed DCSN-Cast outperforms the state-of-the-art CS-based wireless video multicast methods, especially at high packet loss rates.

Toward Building Video Multicast Tree with Congestion Avoidance Capability in Software-Defined Networks

Network congestion is an obstacle to a Quality of Service (QoS) guarantee for online video applications, because it leads to a high packet loss rate and long transmission delay. In the Software-Defined Network (SDN), the controller can conveniently obtain the network topology and link bandwidth use situation. Based on the above advantages, an SDN-based video multicast routing solution, called Congestion Avoidance Video Multicast (CAVM), is proposed in this paper. CAVM obtains overall network topology, monitors available bandwidth resource and measures the link delays based on the OpenFlow, a popular SDN southbound interface. We introduce a novel multicast routing problem, named the Delay-Constrained and Minimum Congestion-Cost Multicast Routing (DCMCCMR) problem, which finds the multicast tree with the lowest congestion cost and a source-destination delay constraint in the SDN environment. The DCMCCMR problem is NP-hard. CAVM uses an algorithm to solve it in polynomial time. Our experimental results confirm that the proposed algorithm can build multicast trees with good congestion avoidance capability

RPL Routing Protocol Performance in Smart Grid Applications Based Wireless Sensors: Experimental and Simulated Analysis

The Advanced Metering Infrastructure (AMI) is one of the Smart Grid (SG) applications that used to upgrade the current power system by proposing a two-way communication system to connect the smart meter devices at homes with the electric control company. The design and deployment of an efficient routing protocol solution for AMI systems are considered to be a critical challenge due to the constrained resources of the smart meter nodes. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was recently standardized by the IETF and originally designed to satisfy the routing requirements of lossy and low power networks like wireless sensors (WSN). We have two kinds of AMI applications, on one hand AMI based WSN and on the other hand AMI based PLC communication. In this paper, we proposed a real and simulated implementation of RPL behavior with proper modifications to support the AMI based WSN routing requirements. We evaluate RPL performance using 140 nodes from the wireless sensor testbed (IoT-LAB) and 1000 nodes using Cooja simulator measure RPL performance within medium and high-density networks. We adopted two routing metrics for path selection: First one is HOP Count (HC) and the second is Expected Transmission Unit (ETX) to evaluate RPL performance in terms of packet delivery ratio; network latency; control traffic overhead; and power consumption. Our results illustrate that routes with ETX calculations in low and medium network densities outperform routes using HC and the performance decreases as the network becomes dense. However, Cooja implementation results provides an average reasonable performance for AMI with high-density networks; still many RPL nodes suffering from high packet loss rates, network congestion and many retransmissions due to the selection of optimal paths with highly unreliable links.

A loss aware MPTCP scheduler for highly lossy networks

Varieties of wireless communication links, such as HSPA+ access in high speed trains, balloon-based aerial wireless networks and satellite Internet connections have high loss rates. In such environments, Multipath TCP (MPTCP) offers a robust solution compared to regular TCP. However, MPTCP and existing schedulers suffer from performance degradation for both constant bit rate (CBR) and bulk traffic. To tackle this challenge, we develop LAMPS, a novel scheduler for MPTCP, which considers both the loss and delay when selecting subflows, and chooses segments based on subflows’ state. The design goal of LAMPS is to achieve a steady performance for different traffic and significantly reduce the unnecessary bandwidth consumption, especially in case of bursty losses. We have implemented LAMPS and evaluated its performance for Dynamic Adaptive Streaming over HTTP (DASH), CBR, and bulk traffic. Our experiment results show that LAMPS preserves application latency, keeps low memory consumption, and significantly reduces extra bandwidth consumption in the presence of high packet loss rate.

Research on distributed dynamic traffic data mining based on vehicular ad hoc network

There are some problems in current traffic data mining methods, such as poor accuracy, high energy consumption, high packet loss rate and lengthy times. A distributed dynamic traffic data mining method based on vehicular ad hoc network is proposed. Before mining, congestion, routing changes, data channel errors and connection interruptions in vehicular ad hoc networks are detected respectively. Particle Swarm Optimisation (PSO) is introduced to realise traffic data classification mining based on vehicular ad hoc networks under network coding and clustering fusion. The simulations results show that the method has high accuracy and efficiency, low-energy consumption and packet loss rate, and high feasibility.