Number Of Packet Losses Research Articles

Enhancing MPTCP performance in wireless networks with PRS-MPTCP

In recent years, there has been a significant increasing demand for wireless networks, particularly mobile communication. Additionally, smart devices like mobile phones and tablets are able to use multiple interfaces simultaneously. In this regard, the concept of Multipath TCP (MPTCP) has been introduced, enabling the utilization of multiple interfaces for concurrent communication. However, packet loss and subflow heterogeneity, especially in wireless networks, cause an increase in Out-of-Order (OfO) packets at the receiver node, which leads to a decrease in the total MPTCP throughput. To address these performance-related challenges, numerous schedulers have been proposed. However, most existing methods have primarily focused on improving performance without adequately considering the impact of packet loss. This research paper provides a comprehensive overview of MPTCP schedulers. Subsequently, we propose a Practical and Robust Scheduler for MPTCP (PRS-MPTCP) with the specific aim of minimizing OfO packets to improve MPTCP performance in wireless systems. The PRS-MPTCP scheduler takes into account various characteristics of each subflow, including RTT, CWND, and the number of packet losses, to effectively decide which packets should be assigned to which subflow. By using Mininet-WiFi emulation, fair comparisons between PRS-MPTCP and the state-of-the-art schedulers have been conducted, considering throughput, the number of OfO packets, and retransmission rates as performance metrics. The evaluation results reveal the profound impact of selected parameters on the behavior of the schedulers and the overall performance of MPTCP. Ultimately, the results demonstrate that PRS-MPTCP guarantees acceptable throughput and achieves lower retransmission rates and fewer OfO packets compared to other methods. In the PRS-MPTCP, the number of OfO packets has decreased by 38 %, 37 %, 45 %, and 44 % compared to BLEST, ECF, RR, and the Default scheduler, respectively.

A Multi-Channel Packet Scheduling Approach to Improving Video Delivery Performance in Vehicular Networks

When working with the Wireless Access in Vehicular Environment (WAVE) protocol stack, the multi-channel operation mechanism of the IEEE 1609.4 protocol may impact the overall network performance, especially when using video streaming applications. In general, packets delivered from the application layer during a Control Channel (CCH) time slot have to wait for transmission until the next Service Channel (SCH) time slot arrives. The accumulation of packets at the beginning of the latter time slot may introduce additional delays and higher contention when all the network nodes try, at the same time, to obtain access to the shared channel in order to send the delayed packets as soon as possible. In this work, we have analyzed these performance issues and proposed a new method, which we call SkipCCH, that helps the MAC layer to overcome the high contention produced by the packet transmission bursts at the beginning of every SCH slot. This high contention implies an increase in the number of packet losses, which directly impacts the overall network performance. With our proposal, streaming video in vehicular networks will provide a better quality of reconstructed video at the receiver side under the same network conditions. Furthermore, this method has particularly proven its benefits when working with Quality of Service (QoS) techniques, not only by increasing the received video quality but also because it avoids starvation of the lower-priority traffic.

A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs.

Vehicle ad hoc networks (VANETs) are a vital part of intelligent transportation systems (ITS), offering a variety of advantages from reduced traffic to increased road safety. Despite their benefits, VANETs remain vulnerable to various security threats, including severe blackhole attacks. In this paper, we propose a deep-learning-based secure routing (DLSR) protocol using a deep-learning-based clustering (DLC) protocol to establish a secure route against blackhole attacks. The main features and contributions of this paper are as follows. First, the DLSR protocol utilizes deep learning (DL) at each node to choose secure routing or normal routing while establishing secure routes. Additionally, we can identify the behavior of malicious nodes to determine the best possible next hop based on its fitness function value. Second, the DLC protocol is considered an underlying structure to enhance connectivity between nodes and reduce control overhead. Third, we design a deep neural network (DNN) model to optimize the fitness function in both DLSR and DLC protocols. The DLSR protocol considers parameters such as remaining energy, distance, and hop count, while the DLC protocol considers cosine similarity, cosine distance, and the node's remaining energy. Finally, from the performance results, we evaluate the performance of the proposed routing and clustering protocol in the viewpoints of packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses. Additionally, we also exploit the impact of the mobility model such as reference point group mobility (RPGM) and random waypoint (RWP) on the network metrics.

DLSMR: Deep Learning-Based Secure Multicast Routing Protocol against Wormhole Attack in Flying Ad Hoc Networks with Cell-Free Massive Multiple-Input Multiple-Output.

The network area is extended from ground to air. In order to efficiently manage various kinds of nodes, new network paradigms are needed such as cell-free massive multiple-input multiple-output (CF-mMIMO). Additionally, security is also considered as one of the important quality-of-services (QoS) parameters in future networks. Thus, in this paper, we propose a novel deep learning-based secure multicast routing protocol (DLSMR) in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO). We consider the problem of wormhole attacks in the multicast routing process. To tackle this problem, we propose the DLSMR protocol, which utilizes a deep learning (DL) approach to predict the secure and unsecured route based on node ID, distance, destination sequence, hop count, and energy to avoid wormhole attacks. This work also addresses key concerns in FANETs such as security, scalability, and stability. The main contributions of this paper are as follows: (1) We propose a deep learning-based secure multicast routing protocol (DLSMR) to establish a high-stability multicast tree and improve security performance against wormhole attacks. In more detail, the DLSMR protocol predicts whether the route is secure based on network information such as node ID, distance, destination sequence, hop count, and remaining energy or not. (2) To improve the node connectivity and manage multicast members, we propose a top-down particle swarm optimization-based clustering (TD-PSO) protocol to maximize the cost function considering node degree, cosine similarity, cosine distance, and cluster head energy to guarantee convergence to the global optima. Thus, the TD-PSO protocol provides more strong connectivity. (3) Performance evaluations verify the proposed routing protocol establishes a secure route by avoiding wormhole attacks as well as by providing strong connectivity. The TD-PSO clustering supports connectivity to enhance network performance. In addition, we exploit the impact of the mobility model on the network metrics such as packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses.

Analisis Performansi Algoritma LEACH-C pada Wireless Sensor Network

Wireless Sensor Network (WSN) is a wireless network infrastructure that requires sensor nodes to process information and communicate. Small sensor nodes generally use batteries as their energy source, causing sensor nodes to have limited power, therefore energy efficiency is needed to extend network life. The solution to overcome these problems is by using the clustering method. In this study the cluster-based routing algorithm used is LEACH-C which can function to allocate overall energy consumption between sensor nodes. In this study, LEACH-C divides the network into several clusters, each of which has a cluster head (CH). cluster head is performed at the base station based on the average energy size of all nodes. The research simulation uses a scenario of changing nodes 50, 100, and 150. The simulation results show that the number of nodes affects the value of energy consumption, the number of data packets received, and the number of packet losses.

Based on Fuzzy Measure Algorithm Message Adaptive Rate Algorithm of Internet of Things

In order to solve the problem that the existing LoRaWAN adaptive data rate control algorithm leads to low data transmission efficiency in the case of network congestion, a method combining a fuzzy logistic regression classifier and an improved adaptive data rate controller adjusting the avoidance time was proposed. The classifier could obtain the predicted congestion state by logistic regression learning. The data rate controller determined the data rate adjustment scheme according to the predicted congestion state. The experimental results showed that when the network congestion occurred in about 12s, the number of packet loss by the LoRaWAN default method was higher than that by the method in the research. The value of ADR_ MSG_CNT of the 15 source nodes in the method was 30 within 0–10 s, while the RCV_ACK_CNT of some nodes was 0. It proved that the method was more efficient than the original LoRaWAN adaptive data rate control algorithm.

Cross region safety monitoring method of distribution secondary system network under Internet of things

In order to ensure the safe transmission of the information of the secondary distribution system across the regional network, this paper studies a security monitoring method of the secondary distribution system across the regional network based on the Internet of things technology and the improved fuzzy clustering algorithm. The Internet of things technology is used to collect the information transmission in cross region network of the secondary power distribution system and store it in the database; Combined with the shadow set to improve the basic fuzzy C-means clustering algorithm, the improved fuzzy C-means clustering algorithm is obtained. The cross region information transmission in the clustering database is divided into two categories: security and risk, and the risk information obtained by clustering is divided into four risk types, so as to realize the security monitoring of information transmission in cross region network of secondary power distribution system. The results show that the average monitoring rate of this method can reach 93.93%, the information collection is efficient and accurate, the number of packet losses is low, and the clustering results are stable and reliable, which can ensure the safe information transmission of cross region network of the secondary power distribution system.

A Hybrid Price Auction-Based Secure Routing Protocol Using Advanced Speed and Cosine Similarity-Based Clustering against Sinkhole Attack in VANETs.

In ad-hoc vehicle networks (VANETs), the random mobility causes the rapid network topology change, which leads to the challenge of the reliable data transmission. In this paper, we propose a hybrid-price auction-based secure routing (HPA-SR) protocol using advanced speed and cosine similarity-based (ASCS) clustering to establish a secure route to avoid sinkhole attacks and improve connectivity between nodes. The main features and contributions of the proposed HPA-SR protocol are as follows. First, the HPA-SR protocol is employed by the first- and second-price auctions to avoid sinkhole attacks. More specifically, using the Markov decision process (MDP), each node can select a kind of auction method to establish the secure route by avoiding the sinkhole attack. Second, the advanced speed cosine similarity clustering protocol that is considered as underlying structure is presented to improve the connectivity between nodes. The ASCS is constructed based on the cosine similarity and distance between nodes using the speed and direction of the nodes. The results of the performance show that the proposed HPA-SR protocol can establish the secure route avoiding the sinkhole attack while the proposed ASCS clustering can support the strong connectivity. Besides, the HPA-SR with ASCS protocol can show better performance than the benchmark protocol in terms of the routing delay, packet loss ratio, number of packet loss, and control overhead.

Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation

In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL) algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with time-sensitive traffic. Since the scheduling policy is a deterministic mapping from channel and queue states to scheduling actions, it can be optimized by using deep deterministic policy gradient (DDPG). We show that a straightforward implementation of DDPG converges slowly, has a poor quality-of-service (QoS) performance, and cannot be implemented in real-world 5G systems, which are non-stationary in general. To address these issues, we propose a theoretical DRL framework, where theoretical models from wireless communications are used to formulate a Markov decision process in DRL. To reduce the convergence time and improve the QoS of each user, we design a knowledge-assisted DDPG (K-DDPG) that exploits expert knowledge of the scheduler design problem, such as the knowledge of the QoS, the target scheduling policy, and the importance of each training sample, determined by the approximation error of the value function and the number of packet losses. Furthermore, we develop an architecture for online training and inference, where K-DDPG initializes the scheduler off-line and then fine-tunes the scheduler online to handle the mismatch between off-line simulations and non-stationary real-world systems. Simulation results show that our approach reduces the convergence time of DDPG significantly and achieves better QoS than existing schedulers (reducing $30\% \sim 50\%$ packet losses). Experimental results show that with off-line initialization, our approach achieves better initial QoS than random initialization and the online fine-tuning converges in few minutes.

Novel Roadside Unit Positioning Framework in the Context of the Vehicle-to-Infrastructure Communication System Based on AHP — Entropy for Weighting and Borda — VIKOR for Uniform Ranking

The positioning of roadside units (RSUs) in a vehicle-to-infrastructure (V2I) communication system may have an impact on network performance. Optimal RSU positioning is required to reduce cost and maintain the quality of service. However, RSU positioning is considered a difficult task because numerous criteria, such as the cost of RSUs, the intersection area and communication strength, affect the positioning process and must be considered. Furthermore, the conflict and trade-off amongst these criteria and the significance of each criterion are reflected on the RSU positioning process. Thus, this work proposes a new RSU positioning framework based on multicriteria decision-making (MCDM) in the context of the V2I communication system. Three stages are completed for this purpose. First, a real-time V2I hardware is developed to collect data. The developed hardware consists of multiple mobile nodes (i.e., cars with sending–receiving hardware devices) and physical RSUs. The RSUs and the devices in the cars are connected via the nRF24L01[Formula: see text]PA/LNA transceiver module with Arduino Uno. Second, seven testing scenarios are identified toward acquiring the required data upon the connection of the V2I devices. Moreover, three evaluation attributes (i.e., number of packet losses [PKL], cost and ratio of intersection area [RIA]) are used to evaluate each scenario. A decision matrix is constructed on the basis of the crossover between ‘RSU positioning scenarios’ and ‘multi-evaluation attributes (i.e., PKL, cost and RIA)’. Third, the RSU positioning scenarios are ranked using MCDM techniques, such as the integrated analytic hierarchy process (AHP), entropy and group Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Furthermore, the Borda voting approach is used to aggregate multiple individual rankings into a uniform and final rank. Results indicate the following: (1) integrating AHP, entropy and VIKOR is effective for solving RSU positioning problems; (2) the VIKOR ranking results for individuals vary; (3) the rank of scenarios obtained from the group-VIKOR-based Borda voting context shows that the second scenario, which consists of four RSUs distributed along the street with a maximum distance of 200[Formula: see text]m between them and 2-m high antennas, is the best in terms of optimally placing the RSUs; and (4) significant differences are observed amongst the scores of the groups, indicating that the ranking results are valid.

Congestion Control in Cable Network Transmission using Novel RED Algorithm

Congestion is an important issue in cable network where transmitted packets to the network are much more than holding capacity of network. Each network needs the maximum number of successful packets to reach the destination with less number of packet losses and low time delay with better fairness. There are several congestion control algorithms which have been studied to keep the network stable. They employ various techniques from which the buffer or queue management used by the router is one significant issue to control congestion. The Active Queue Management algorithms like RED controls the queue size by dropping or marking the packets before buffer becomes full . In this paper a new algorithm has been proposed and the results are compared with existing RED algorithm using network simulator (NS-2) in a responsive environment of the cable network.

Prevention of Stealthy Attacks through Privacy Mechanism in Wireless Ad hoc Networks

Objectives: Stealthy attacks are a kind of attacks which injects route fabrication information and producing false identification in network. In this paper, we evaluate these two stealthy attacks against a privacy mechanism as Distinct Authentication Key Approach (DAKA) in MANET. Methods/Statistical Analysis: Wireless networks are becoming the most popular in today communication systems, where users prefer to have wireless connectivity regardless of its geographic location. However, the openness of wireless communications increases the threat to MANET under its conditions. It is very challenging to validate false identify in a stealthy node behaviour as it gives a normal impression and in case of route fabrication they modify the routing path which leads a high number of packet loss. The DAKA provides a Secure Route Discovery Mechanism and data routing through a privacy and certificate authentication methodology. Findings: We evaluate the proposed approach by measuring Packet Delivery Ratio, Avg. End-to-End Delay, Control Overhead and Packet Drop Ratio with varying the number of malicious nodes in a fixed number of nodes and traffic. Application/ Improvements: The simulation experiment result shows successfully alleviation of the malicious nodes and achieves the needed performance.

Prioritizing and scheduling messages for congestion control in vehicular ad hoc networks

Vehicular Ad Hoc Networks (VANets) is considered as a technology which can increase safety and convenience of drivers and passenger. Due to channel congestion in high density situation, VANets’ safety applications suffer of degradation of performance. In order to improve performance, reliability, and safety over VANets, congestion control should be taken into account. However, congestion control is a challenging task due to the special characteristics of VANets (e.g. high mobility, high rate of topology change, frequently broken rout, and so on). In this paper, DySch and TaSch strategies are proposed. Those strategies assign priorities to the safety and service messages based on the content of messages (static factor), state of network (dynamic factor) and size of messages. DySch and TaSch strategies schedule the messages dynamically and heuristically, respectively. Their performance is investigated using highway and urban scenarios while the average delay, average throughput, number of packet loss, packet loss ratio, and waiting delay in queues are considered. Simulation results show that DySch and TaSch strategies can significantly improve the performance of VANets in comparison to the best conventional strategies. Employing the proposed strategies to control congestion in VANets helps increase reliability and safety by giving higher priority to the safety messages.

A Novel Congestion Avoidance Technique for Simultaneous Real-Time Medical Data Transmission.

The use of wireless body sensor networks (WBSN) in medical services aims at providing continuous monitoring of patients' physiological data. However, the scarce resources in WBSN nodes limit their capabilities to cope with massive traffic during multiple, simultaneous data transmissions. This will create a high tendency for congestion, causing severe performance degradation. Congestion may lead to high number of packet loss and unbounded delay which are critical and may lead to wrong diagnosis. This paper, therefore, aims at improving this limitation using a novel congestion avoidance technique to avoid losing real-time and life-critical medical data (e.g., electrocardiogram and electroencephalography) which are vital for diagnosis. The main idea is to integrate the existing rate control scheme of relaxation theory (RT) with a method known as max-min fairness (MMF) to achieve better performance. The MMF can be accomplished using a progressive filling algorithm, which cuts-down excessive sending rates that may overwhelme the limited buffer in WBSN. This paper builds upon our prior study, which provides a preliminary analysis of RT technique in single node. Our current technique integrates the MMF phase to enhance RT performance when the transmission rates exceed certain threshold. Performance evaluation on RT-MMF technique shows remarkable performance improvements, while maintaining the desired quality of service.

Data Recovery in Wireless Sensor Networks using Network Coding

Data transportation over resources constraint and noisy channel of wireless sensor network (WSN) is very challenging in term of guaranteeing the data survival along the transmission. However, with the convergence of different research areas such as routing, source and channel coding techniques, the WSN technology has successfully been tremendously developed. This paper proposes an on-the-fly data recovery (ODR) scheme using network coding in order to enhance the robustness of the network against packet loss. Along with the ODR scheme, the packet loss formulation is presented while a network model for a network coding designed is also introduced namely for erasure channel. The data generated by the sources are transferred to the destination through relay nodes via three transmission paths. In ODR process, the lost packet is recovered by a relay node by listening to the transmission of two adjacent nodes and performing the XOR operation on the listened packets. We provide the analytical study on network coding performance and conducting the simulation experiment to verify it. In the simulation studies, we have also compared the performance of the network using network coding with and without packet recovery. The result shows that the number of packet loss has been reduced significantly using the proposed scheme compared to the network with normal network coding.

Mathematical Model for Dynamic Pump-Wavelength Selection Switch

This paper presents a mathematical model based on dynamic pump-wavelength selection for an optical packet switch (OPS). In the OPS, multiple packets that carry the same wavelength from different input ports could be addressed to the same output port at the same time slot. This condition is called wavelength contention. Of those contended packets, only one is forwarded to the output fiber while the others are dropped. Parametric wavelength conversion is used to convert the contended wavelengths into available non-contending wavelengths. The OPS based on the dynamic pump-wavelength selection scheme, where the pump-wavelengths are adjusted based on the requests in every time slot, uses a heuristic matching algorithm to minimize the number of packet losses. However, there is no guarantee that the heuristic algorithm outputs the optimum result. The mathematical model presented in this paper is used to confirm the performance of the heuristic matching algorithm for the DPS-based OPS. A simulation shows that the heuristic matching algorithm achieves the same performance as the optimum solution provided by the mathematical model.

Mathematical performance analysis for HCoP-B reactive fast handover mechanisms for the nested mobile networks

Purpose– In the past, by adopting the handover prediction concept of the fast mobile IPv6, the authors have proposed a cross-layer architecture, which was called the proactive fast HCoP-B (FHCoP-B), to trigger layer 3 HCoP-B route optimization flow by 802.11 and 802.16 link events before the actual layer 2 handover of a mobile subnet in the nested mobile network (NEMO) occurs. In this way, proactive FHCoP-B has shortened its handover latency and packet loss. However, there are two scenarios where proactive FHCoP-B cannot normally complete its operations due to fast movements of the NEMO during handover. The paper aims to discuss these issues.Design/methodology/approach– In this paper, the authors will propose efficient reactive FHCoP-B flows for these two scenarios to support fast and seamless handovers. The authors will further extend the analytical model proposed for mobile IPv6 to investigate four performance metrics of proactive and reactive FHCoP-B, HCoP-B and two well-known NEMO schemes with the radio link protocol (RLP), which can detect packet losses and performs retransmissions over the error-prone wireless link.Findings– Through intensive simulations, the authors conclude that FHCoP-B outperforms HCoP-B and the other two well-known NEMO schemes by achieving the shortest handover latencies, the smallest number of packet losses and the fewest playback interruption time during handover only with few extra buffer spaces, even over error-prone wireless links of the nested NEMO.Originality/value– This paper has three major contributions, which are rare in the NEMO literature. First, the proactive FHCoP-B has been enhanced as the reactive one to handle two fast handover scenarios with RLP for the nested NEMO. Second, the reactive FHCoP-B supports seamless reactive handover for the nested NEMO over error-prone wireless links. Third, mathematical performance analyses for two scenarios of reactive FHCoP-B with RLP over error-prone wireless links have been conducted.

Cluster-Head Selection on Link Quality Routing Protocol for Wireless Sensor Network

recent years, the researchers do lot of research on Wireless Sensor Network. But the most research going on the prolongation of the lifetime of the WSN and declined the energy consumed by the sensors. There are lots of protocols which are generated for routing in WSN but these have some limitations. In this article analyzes the cluster head selection phase of LEACH protocol and presents the improved approach of LEACH i.e. CSLQ (Cluster-head based on link quality) that improves the lifetime of the network. In CSLQ the cluster -head is selected on the basis of link quality so that number of packet loss is decreased as compared to the LEACH protocol. In this paper evaluated both modified LEACH (9) and CSLQ through extensive simulations using NS2 (Network Simulator 2) which shows that CBLQ protocol performs better than the LEACH protocol.

Assessment of the IEEE 802.11e EDCA Protocol Limitations when Dealing with Real-Time Communication

The timing behavior of the EDCAmechanism defined in the IEEE 802.11e standard is analyzed. More specifically, the target of this paper is to evaluate the limitations of the highest priority level of the EDCA mechanism (voice category) when supporting real-time (RT) communication. By RT communication, we mean small-sized packets generated in periodic intervals that must be delivered before the end of the message stream period. Otherwise, the message is considered to be delayed and a deadline loss occurs. We have assessed the EDCA mechanism considering an open communication environment, where both RT and non-RT stations share the same frequency band. Furthermore, a realistic error-prone model channel was used to measure the impact of interferences against an error-free channel. We show that, for most part of the evaluated scenarios, when using the default parameters of the EDCA mechanism both the number of packet losses and the average packet delays forecast an unacceptable number of deadline losses. However, if adequate Contention Windows (CW) parameters are configured in the set of RT stations, it becomes possible to adequately handle RT traffic. As a conclusion of this paper, we present some potential future directions toward improved QoS in wireless networks.

EHSTCP: Enhanced TCP in High-Speed Networks

TCP doesn't perform well in high bandwidth-delay product networks,and its main limitations are low throughput and large oscillation of congestion window.HSTCP solves the primary limitations of regular TCP,but there exists large number of packet loss upon congestion epoch and serious RTT unfairness with drop-tail gateway.To overcome the above deficiency,this paper proposes an improved algorithm,called EHSTCP,which switches between two congestion avoidance modes in congestion avoidance phase.To locate the switch point,this paper presents a new prediction method for end-to-end available bandwidth using history congestion window dynamics.Simultaneously,it introduces the RTT fairness factor to eliminate the serious RTT unfairness in HSTCP.NS2 simulation results validate the effectiveness of the proposed scheme.