Packet Loss Research Articles

Improving Throughput and Sum Secrecy Rate in Wireless Networks Against Illegal Intelligent Reflecting Surface (IIRS) Based Jamming Attacks

Illegal intelligent reflecting surfaces (I-IRSs), i.e., the illegal deployment and utilization of IRSs, impose serious harmful impacts on wireless networks. The existing I-IRS-based illegal jammer (IJ) requires channel state information (CSI) or extra power or both, and therefore, the I-IRS-based IJ seems to be difficult to implement in practical wireless networks. Due to continued sharing of resources, wireless networks often come under security attacks, most common of which are eavesdropping attacks. In the case of eavesdropping attacks, deliberately designed random eavesdropping data is added to the channel. These eavesdropping along with noise result in packet losses and low throughput, degrading the overall performance of the cognitive network. In this work, a security aware jamming and eavesdropping rejection mechanism is proposed which detects suspicious signals in the channel frequency response and employs discrete equalization to recover transmitted data. The proposed approach uses a chaos based FFH protocol for possible adversarial eavesdropping attacks. The error rate and average sum rate show that the proposed system outperforms the exiting systems in terms of the performance metrics. Keywords:- Illegal Intelligent Reflecting Surface (IIRS), Jamming Attack, Packet Intercept. Error Rate, Sum Rate

Key technologies of smart antenna in WLAN based on adaptive array

In order to improve the communication quality and transmission rate of WLAN, the key technologies of smart antenna in WLAN based on adaptive array are proposed. The adaptive array is used to suppress the WLAN interference signal, and the feedback from the output is used to adjust the weight loaded on the signal, and the optimal weight corresponding to the linear constraint minimum variance is obtained to achieve beamforming optimization. On this basis, the optimal antenna selection algorithm and the sub-optimal antenna selection algorithm are designed, and the sub-channel matrix with the maximum capacity can be obtained by repeated iterative calculation, so as to realize the smart antenna selection and further optimize the performance of WLAN. Experimental results show that the smart antenna signal has 100% coverage in WLAN compared with conventional methods. When the transmission rate is greater than 35 mb/s, the delay of the study method is not jitter compared to the contrast method. When the transmission rate is greater than 35 mb/s, the packet loss rate of the proposed method remains stable, while the packet loss rate of the two comparison methods begins to increase. When the transmission rate is greater than 12 mb/s, the throughput of the proposed method is more stable than the other methods. In conclusion, this method has the characteristics of large signal coverage, low delay, low packet loss rate and high throughput, and can be widely used in practice. The contribution of this study is that the weight of the antenna array can be dynamically adjusted through the adaptive algorithm, thus optimizing the quality of the received signal and improving the receiving sensitivity.

Reachable set estimation of multi-agent systems under packet losses and deception attacks

Reachable set estimation of multi-agent systems under packet losses and deception attacks

Improved smart energy-based routing approach for IoT networks in wireless sensor nodes

An emerging communication and network domain called the Internet of Things (IoT) uses nodes that function like actual real-world objects. Both moving and fixed nodes are possible for these. Maintaining node stability or improving inter-node connectivity is the main task for every IoT network. These networks are portable, leading to an overwhelming production of control overhead transmissions which can be reduced with the help of network clustering. In order to extend the network’s lifespan, it is therefore necessary to improve the power usage of sensor nodes. The goal of this research work is to improve network lifetime, increase throughput, reduce packet latency or packet loss, and even expand to addressing threatening nodes in order to cope with the issues of IoT-based sensor systems. The proposed Improved Smart Energy-Based Routing Protocol (ISERP), once combined with a wireless energy harvesting (WEH) unit, improves the node’s lifespan, the network’s quality of services (QoS) amid higher unequal congestion, and those aspects controlling its availability of energy. In the framework of distributed IoT, this protocol meets the standards of QoS through employing hardware-based link quality estimation and accomplishing very high efficiency in terms of energy in comparison with existing routing methods. The sink node chooses both the cluster head (CH) and cluster gateway (CG) by considering the estimation of its cost function. Simulation findings indicate that this proposed technique exhibits more network longevity along with lower consumption of energy compared with other existing cluster-based routing algorithms. Specifically, with the proposed ISERP, CHs use 33% less energy in comparison to existing techniques, and 40% of all nodes remain active until the end of the phase.

Resilient Integrated Control for AIOT Systems under DoS Attacks and Packet Loss

This paper addresses bandwidth limitations resulting from Denial-of-Service (DoS) attacks on Artificial Intelligence of Things (AIOT) systems, with a specific focus on adverse network conditions. First, to mitigate the impact of DoS attacks on system bandwidth, a novel model predictive control combined with a dynamic time-varying quantization interval adjustment technique is designed for the encoder–decoder architecture of AIOT systems. Second, the network state is modeled to represent a Markov chain under suboptimal network conditions. Furthermore, to guarantee the stability of AIOT systems under random packet loss, a Kalman filter algorithm is applied to precisely estimate the system state. By leveraging the Lyapunov stability theory, the maximum tolerable probability of random packet loss is determined, thereby enhancing the system’s resilient operation. Simulation results validate the effectiveness of the proposed method in dealing with DoS attacks and adverse network conditions.

Analysis of Unmanned Aerial Vehicle Communication Performance with Optic Sensor Integration

This paper presents a novel algorithmic framework to enhance unmanned aerial vehicle (UAV) communication performance through the adaptive integration of optical sensor data. The framework features a dynamic sensor integration algorithm (DSIA) that optimizes sensor bitrates based on real-time bandwidth availability and mission-driven priorities. An adaptive compression scheme works alongside the DSIA to maintain high data fidelity and resolution at lower bitrates. Together, these algorithms provide an efficient and scalable solution for managing high-volume UAV sensor data under varying conditions. The efficiency gains are demonstrated through simulations highlighting the framework's ability to reduce packet loss and latency while maximizing image quality for a given communication bandwidth constraint. The scalable architecture ensures robustness across diverse UAV platforms, sensor types, and operational scenarios. Simulation results validate up to a 75% reduction in latency and a 45% improvement in image resolution compared to conventional fixed bitrate approaches. By optimizing sensor data relevancy and transmission fidelity, this framework enables more effective UAV communication and enhances mission performance.

A lightweight BPSO mechanism for topology reconfiguration in data-driven IIoT plants

Wireless Sensor Networks in large-area monitoring applications may be required to deal with emergency events, i.e., events from regions that suddenly require close attention. Nodes in these regions may demand increased data generation rates, and their communication with the base station (BS) should be prioritized. In this sense, a significant challenge is how to reconfigure the network to create paths that offer adequate Quality of Service (QoS) between these sensor nodes and the BSs. Depending on the network’s size, the computational complexity of this problem may become unacceptable; but a meta-heuristic approach could handle it. This work describes a lightweight Binary Particle Swarm Optimization (BPSO) mechanism for the topology reconfiguration of data-driven cluster-tree networks based on IEEE 802.15.4 standard. The goal of this mechanism, called CALiPSO, is to reduce the computational complexity when searching for a suitable network topological configuration that improves network QoS metrics. A simulation assessment and comparison with other approaches were conducted. It was demonstrated that CALiPSO can reconfigure a network of 125 nodes and create an appropriate cluster-tree with fewer cluster-heads in hotspot regions, while improving network QoS. Compared with heuristic and optimization methods, CALiPSO selects around 42% to 55% fewer CHs and forms trees 22% less depth. Concurrently, it diminishes communication delays by about 5% and decreases packet losses by 7.5%, without jeopardizing energy consumption.

Counteracting Packet Loss in Fingerprint-Based Indoor Positioning via Spatially Regularized Entropy and Ground-Truth Prior Variational Inference

Counteracting Packet Loss in Fingerprint-Based Indoor Positioning via Spatially Regularized Entropy and Ground-Truth Prior Variational Inference

Security dissipative control for interval type‐2 fuzzy hidden Markovian jump systems under event‐triggered scheme

AbstractThis article presents a new design approach for resilient fault‐tolerant event‐triggered (ET) based security control of Markovian jump nonlinear systems (MJNSs) in the presence of nonperiodic denial‐of‐service (DoS) attacks. The approach is developed based on interval type‐2 (IT‐2) fuzzy models of the plant. The state error produced by data packet loss is turned into a parameter in the proposed ET condition during nonperiodic DoS attacks. The uncertain triggering variable is considered in the resilient fault‐tolerant ET condition for the MJNSs. In contrast to the usual strategy, the fault‐tolerant ET strategy is designed to send the required data packets to the controller during nonperiodic DoS attacks, which significantly contributes to the minimization of performance loss. Dissipative, passive, and ‐control schemes are all covered in a unifying framework by using a generalized performance index. Plant and controller modes are considered under mismatched membership functions to regulate asynchronous phenomena. Sufficient conditions depending on the membership functions that guarantee the stability of the closed‐loop system are derived. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed approach.

Enhancing Quality of Service and Energy Efficiency in Wireless Sensor Networks through TDMA-Based Topology and Cross-Layer Approaches

It is necessary to continuously monitor the environment with the sensors in delay-sensitive wireless sensor network applications. The continuous monitoring operation results in higher energy consumption by the sensor nodes; wireless sensor networks are also subject to numerous difficulties, including latency, packet loss, and throughput. These are the most important constraints in wireless sensor networks (WSNs) and need to improve for better quality communication. In this research, quality of service is related to numerous systems that have very sensitive parameters such as end-to-end delay, packet loss, throughput, energy consumption, latency, and average delay. Therefore, it is necessary for the sensors to transmit reliable data within a reasonable timeframe. To find out better communication results, we have used the TDMA (Time Division Multiple Access) approaches in our paper to improve packet delivery ratio (PDR), latency, and throughput. The NS2 simulator is used to test communication between nodes. Latency, the packet delivery ratio (PDR) of transferring data packets, and throughput are measured. The TDMA approach is used in our proposed topology, and we can see a specific area where nodes are connected to communicate with one another. Each node is communicating with the other node using two different paths. Each node's bandwidth, latency, and PDR will be different. Hence, in one path, a TDMA approach is implemented, which results in improved node performance in terms of latency, packet delivery ratio, and bandwidth.

A Recursive Non-Uniform Sampling Estimator for Asynchronous Nonlinear Systems.

In this paper, we consider the problem of asynchronous estimation in the presence of packet losses for the randomly sampling nonlinear system. Packet losses occur at the control input and at the measurement side. Firstly, the synchronization of the asynchronous sampling system is realized by weighting the state of the adjacent state update points. Secondly, the projection theorem is used to estimate the system state at the sampling time. Due to modeling errors and unmodeled dynamics, obtaining an accurate dynamic model is challenging. Therefore, observation inference based on interpolation techniques is proposed to solve the asynchronous estimation problem. Furthermore, the algorithm is extended to multi-sensor systems to obtain a distributed fusion estimator. Finally, simulation experiments are conducted to validate the effectiveness of the algorithm.

Detection of black hole attacks in vehicle-to-vehicle communications using ad hoc networks and on demand protocols

PurposeThe potential of vehicle ad hoc networks (VANETs) to improve driver and passenger safety and security has made them a hot topic in the field of intelligent transportation systems (ITSs). VANETs have different characteristics and system architectures from mobile ad hoc networks (MANETs), with a primary focus on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. But protecting VANETs from malicious assaults is crucial because they can undermine network security and safety.Design/methodology/approachThe black hole attack is a well-known danger to VANETs. It occurs when a hostile node introduces phony routing tables into the network, potentially damaging it and interfering with communication. A safe ad hoc on-demand distance vector (AODV) routing protocol has been created in response to this issue. By adding cryptographic features for source and target node verification to the route request (RREQ) and route reply (RREP) packets, this protocol improves upon the original AODV routing system.FindingsThrough the use of cryptographic-based encryption and decryption techniques, the suggested method fortifies the VANET connection. In addition, other network metrics are taken into account to assess the effectiveness of the secure AODV routing protocol under black hole attacks, including packet loss, end-to-end latency, packet delivery ratio (PDR) and routing request overhead. Results from simulations using an NS-2.33 simulator show how well the suggested fix works to enhance system performance and lessen the effects of black hole assaults on VANETs.Originality/valueAll things considered, the safe AODV routing protocol provides a strong method for improving security and dependability in VANET systems, protecting against malevolent attacks and guaranteeing smooth communication between cars and infrastructure.

AN EVALUATION OF THE POWER SUPPORT INTERNET INFRASTRUCTURE OF MAKASSAR CITY IN TELEMEDICINE FRAME

AN EVALUATION OF THE POWER SUPPORT INTERNET INFRASTRUCTURE OF MAKASSAR CITY IN TELEMEDICINE FRAME

Reinforcement learning-based SDN routing scheme empowered by causality detection and GNN.

In recent years, with the rapid development of network applications and the increasing demand for high-quality network service, quality-of-service (QoS) routing has emerged as a critical network technology. The application of machine learning techniques, particularly reinforcement learning and graph neural network, has garnered significant attention in addressing this problem. However, existing reinforcement learning methods lack research on the causal impact of agent actions on the interactive environment, and graph neural network fail to effectively represent link features, which are pivotal for routing optimization. Therefore, this study quantifies the causal influence between the intelligent agent and the interactive environment based on causal inference techniques, aiming to guide the intelligent agent in improving the efficiency of exploring the action space. Simultaneously, graph neural network is employed to embed node and link features, and a reward function is designed that comprehensively considers network performance metrics and causality relevance. A centralized reinforcement learning method is proposed to effectively achieve QoS-aware routing in Software-Defined Networking (SDN). Finally, experiments are conducted in a network simulation environment, and metrics such as packet loss, delay, and throughput all outperform the baseline.

Evaluation of BLE-based audio broadcasting under probabilistic interference

Wireless Bluetooth audio communication has become an inherent part of everyday life, from listening to a podcast on our headphones to streaming music on multiple speakers. Up until now, broadcasting audio to multiple receivers has always required a proprietary implementation. Therefore, recent advances in the specification allow Broadcast Audio streams to be set up on top of Bluetooth Low Energy (BLE). To cope with the unpredictability of wireless media, audio frame retransmission opportunities are provided. However, determining the applicable number of retransmissions for a broadcast stream that is exposed to volatile environmental conditions, is a complex research challenge. This paper presents a model that is capable of simulating a BLE broadcast stream schedule, exposed to various environmental conditions while using a variable number of audio frame retransmissions. The evaluation employs several existing Packet Loss Concealment (PLC) techniques to cope with audio frame losses. The results provide insights into the impact of various frame loss patterns on the audio quality and intelligibility of broadcasted speech. The more advanced PLC techniques can handle a higher frame loss rate threshold. The analysis also shows that large audio frames requiring fragmentation exhibit a higher amount of frame loss for the same BLE packet loss rate and that overlap with in-use 802.11 channels can lead to a large variability in frame loss behavior. The model provides a baseline for the next research challenge related to continuous management of Broadcast Audio streams operating under volatile environmental conditions.

Optimal control for networked control system with Markovian packet loss and delay

AbstractThis paper focuses on the optimal output‐feedback control and stabilization of a networked control system, where both time delay and packet loss are involved. Different from most previous work, the packet loss is Markovian, and the time delay occurs between the actuator and controller. The difficulty is rooted in the failure of the separation principle. The main tools are solving the delayed forward‐backward stochastic difference equations (D‐FBSDEs) and convergent analysis. Resorting to solving D‐FBSDEs and completing the square, we acquire the analytical solution, including the necessary and sufficient solvability condition and the explicit controller, of the finite‐horizon optimal control problem. Based on the finite‐horizon result and convergent analysis, we then obtain the analytical solution of the infinite‐horizon optimal control and stabilization problems. In addition, by analyzing the convergence of the optimal estimator, we establish a constraint relationship between the packet loss probability and the system matrix . The results are evaluated by numerical examples.

Analisis Jaringan 4G untuk Aplikasi Smart Ambulance

Intelligent Transport System (ITS) is an intelligent traffic system used for traffic management and engineering. The ITS network uses inter-vehicle telecommunication technology. The city of Semarang is one of the cities that will establish ITS technology. One area that applies technology is health. The telecommunications system applied in the health sector today allows medical staff to treat patients in ambulances before arriving at the hospital. This communication system needs to be supported by a good cellular network so that data can be sent to the server. This study aims to measure QOS (Quality of Service) including the characteristics of delay, packet loss, jitter, and throughput from various cellular providers. The results of data measurements using wireshark software with TIPHON standard guidelines obtained an average value for delay obtained by Provider A with the best result of 86.22 ms. The average value for packet loss obtained from the measurement results from the Penggaron Terminal to the City Hall Bus Stop was obtained by Provider A with the best result of 0.474% d for the City Hall Bus Stop to the Penggaron Terminal obtained by Provider B with the best result of 0.574%. The average value of jitter obtained from the measurement results from the Penggaron Terminal to the City Hall Bus Stop was obtained by Provider B with the best result of 0.42 ms, for the City Hall Bus Stop to the Penggaron Terminal, Provider A was obtained with the best result of 0.57 ms. The average throughput value obtained from the measurement results for Penggaron Terminal to City Hall Bus Stop was obtained by Provider C with the best result of 6631 Kbps, for City Hall Bus Stop to Penggaron Terminal obtained by Provider B with the best result of 6339.5 Kbps. Based on this, knowing the value of delay, packet loss, jitter and throughput is expected to be a consideration for the development of a smart ambulance system in the city of Semarang. There are two measurement points, namely the Penggaron Terminal to the City Hall Bus Stop and the City Hall Bus Stop to the Penggaron Terminal using the Semarang BRT.

Enhancing security in wireless sensor networks: A fusion of deep learning and energy-efficient routing

The development of tiny sensing nodes efficient for wireless communication in Wireless Sensor Networks (WSNs) can be attributed to the rapid advancements in processors and radio technology. Data transmission occurs through multi-hop routing in WSN, which relies on nodes’ cooperation. The collaboration between nodes has rendered these networks susceptible to various attacks. It is imperative to employ a security scheme to evaluate the dependability of nodes in distinctive malicious nodes from non-malicious nodes. In recent years, there has been a growing significance placed on security-based routing protocols with energy constraints as valuable mechanisms for enhancing the security and performance of WSNs. A novel solution called the Deep Learning-based Hybrid Energy Efficient and Security System (DL-HE2S2) is introduced to address these challenges. The research workflow encompasses various essential stages, namely the deployment of nodes, the creation of clusters, the selection of cluster heads, the detection of malevolent nodes within each group, and the determination of optimal paths intra- and inter-clusters employing the routing algorithm for efficient packet transmission. The design of the algorithm is focused on achieving energy efficiency and enhancing network security while also taking into account various performance metrics, including a mean network lifetime of 187.244 hours, a throughput of 59.88 kilobits per second, an end-to-end latency of 11.939 milliseconds, a packet loss of 14.9%, a packet delivery ratio of 99.194%, network security at 92.026%, and energy usage of 19.424 J. This research examines the algorithm’s scalability and efficiency across various network sizes using a Network Simulator (NS-2). DL-HE2S2 offers valuable insights that can be applied to practical implementations in multiple applications.

A multi-objective optimized OLSR routing protocol.

The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics.

DCCS: A dual congestion control signals based TCP for datacenter networks

As cloud computing advances, datacenter networks (DCNs) face unprecedented pressure due to the increasing prevalence of high bandwidth and low delay applications. Current reliable transmission protocols such as TCP harness packet loss, network delay, and Explicit Congestion Notification (ECN) for congestion control, aiming to maintain low latency and high throughput. We investigate the efficacy of ECN-based protocols against incast congestion in one-hop networks and delay-based protocols for maintaining low end-to-end delay in multi-hop networks, at the expense of throughput fairness. Thus, we introduce a novel TCP protocol, Dual Congestion Control Signals (DCCS), merging ECN and delay signals for a broader congestion control mechanism. DCCS effectively handles burst traffic, prevents packet loss, ensures low end-to-end delay, and resolves throughput unfairness by determining a precise minimum round-trip time (RTT) for each flow through a drain signal. Simulations on ns-3 confirm that DCCS surpasses ECN-based (i.e., DCTCP), delay-based (i.e., DC-Vegas and Swift), and hybrid-based protocols (i.e., EAR) in fat-tree topology under realistic workload, cutting down Flow Completion Time (FCT) by 3.8%–20.5%. This substantiates DCCS’s competency in providing high-quality service for DCNs.